commit WAR

.gitattributes

@@ -32,3 +32,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+datasets/OnlineNewsPopularity/OnlineNewsPopularity.csv filter=lfs diff=lfs merge=lfs -text
+datasets/traffic_flow_forecasting/traffic_dataset.mat filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,24 @@
+# Representativity-based active learning for regression using Wasserstein distance and GroupSort Neural Networks
+
+You will find in this repository the codes used to test the performance of the WAR model on a fully labeled dataset
+
+**WAR-notebook** : you can run the algorithm from there and change the desired parameters
+
+
+### WAR directory
+
+**Experiment_functions.py** : functions used to vizualise information about WAR process (loss, metrics, points queried every rounds...).
+
+**Models.py**: Definition of the two neural networks h and phi.
+
+**dataset_handler.py**: Import and preprocess datasets.
+
+**full_training_process.py**: main function.
+
+**training_and_query.py**: function to run one round (one training and querying process).
+
+
+## Abstract
+This paper proposes a new active learning strategy called Wasserstein active regression (WAR) based on the principle of distribution-matching to measure the representativeness of our labeled dataset  compared to the global data distribution. We use GroupSort Neural Networks to compute the Wasserstein distance and provide theoretical foundations to justify the use of such networks with explicit bounds for their size and depth. We combine this solution with another diversity and uncertainty-based approach to sharpen our query strategy. Finally, we compare our method with other solutions and show empirically that we consistently achieve better estimations with less labeled data.
+
+

WAR-notebook.ipynb

@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Notebook to test WAR performances on a fully labelled dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import itertools\n",
+    "import time\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import torch\n",
+    "import torch.optim as optim\n",
+    "\n",
+    "from WAR.Models import NN_phi,NN_h_RELU\n",
+    "from WAR.training_and_query import WAR\n",
+    "from WAR.dataset_handler import myData,import_dataset,get_dataset\n",
+    "from WAR.Experiment_functions import *\n",
+    "from WAR.full_training_process import full_training,check_num_round\n",
+    "from sklearn.cluster import KMeans\n",
+    "\n",
+    "from sklearn.decomposition import PCA\n",
+    "\n",
+    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
+    "print(f\"Using {device} device\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "#choosing dataset and splitting it with the desired testset proportion\n",
+    "# for now dataset=\n",
+    "#\"boston\",\"airfoil\",\"energy1\",\"energy2\",\"yacht\"\n",
+    "#,\"concrete_slump\",\"concrete_flow\",\"concrete_compressive\",x_squared\",\"news_popularity\"\n",
+    "\n",
+    "X_train,X_test,y_train,y_test=get_dataset(proportion=0.2,dataset=\"boston\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#2D PCA visualization of the data\n",
+    "#kmeans = KMeans(n_clusters=nb_initial_labelled_datas, init='k-means++', n_init=10).fit_predict(X_train)\n",
+    "pca = PCA(n_components=2)\n",
+    "transformed = pca.fit_transform(X=X_train)\n",
+    "print(f\"{round(sum(pca.explained_variance_),4)*100}% variance explained\")\n",
+    "plt.figure(figsize=(8.5, 6))\n",
+    "plt.scatter(x=transformed[:, 0], y=transformed[:, 1]#,c=kmeans\n",
+    "           )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# WAR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "total_epoch_h=100 # number of epochs to train h each round\n",
+    "total_epoch_phi=100 # number of epochs to train phi each round \n",
+    "num_elem_queried= int(0.02*X_train.shape[0]) # number of elem queried each round  \n",
+    "nb_initial_labelled_datas = int(0.02*X_train.shape[0]) #nb of labelled datas at round 0\n",
+    "init_method=\"k_mean\" # how the initial data will be chosen. \"random\" or \"k-means\" \n",
+    "second_query_strategy=\"loss_approximation\" # query strategy assisting our distribution-matching criterion. \"loss_approximation\" or None for now\n",
+    "lr_h=0.001  # learning rate h \n",
+    "lr_phi=0.01  # learning rate phi  \n",
+    "weight_decay=0.001 # L2 regularization on h\n",
+    "\n",
+    "batch_size_train=len(X_train) # size of the batch during the training process  #len(X_train)\n",
+    "num_round=500  # number of rounds\n",
+    "num_round=check_num_round(num_round,len(y_train),nb_initial_labelled_datas,num_elem_queried)\n",
+    "\n",
+    "\n",
+    "reset_phi=False  # reset the training of phi each round or not\n",
+    "reset_h=False  # reset the training of h each round or not\n",
+    "\n",
+    "reduced=True  # if true (recommended),\n",
+    "#the heterogeneity and representativity criteria will have the same standard deviation,\n",
+    "#to give them the same weight in the query process. This give us more control on our querying strategy\n",
+    "\n",
+    "eta=3   # weight of the representativity criterion. if relatively low (<3) can lead WAR to query too many outliers\n",
+    "# cnst_t3phi>3 recommended, can be put higher if there are a lot of outliers in the data distribution \n",
+    "\n",
+    "show_losses=False   # show T1 and T2 losses each rounds in a graph\n",
+    "show_chosen_each_round=False   # show which data have been chosen each round in a 2D PCA representation of the data\n",
+    "\n",
+    "dim_input=X_train.shape[1]\n",
+    "\n",
+    "start=time.time()\n",
+    "\n",
+    "n_pool = len(y_train)\n",
+    "n_test = len(y_test)\n",
+    "idxs_lb = np.zeros(n_pool, dtype=bool)\n",
+    "idxs_tmp = np.arange(n_pool)\n",
+    "\n",
+    "\n",
+    "if init_method==\"random\":\n",
+    "    # Generate the initial labeled pool\n",
+    "    np.random.shuffle(idxs_tmp)\n",
+    "    idxs_lb[idxs_tmp[:nb_initial_labelled_datas]] = True\n",
+    "    \n",
+    "elif init_method==\"k_mean\":\n",
+    "    init_indices=[]\n",
+    "    kmeans = KMeans(n_clusters=nb_initial_labelled_datas, init='k-means++', n_init=10).fit(X_train)\n",
+    "    for i in range(nb_initial_labelled_datas):\n",
+    "        xsc = kmeans.cluster_centers_[i]\n",
+    "        ind = np.argmin(((X_train - xsc) ** 2).sum(axis=1))\n",
+    "        init_indices.append(ind)\n",
+    "    idxs_lb[init_indices] = True\n",
+    "\n",
+    "h=NN_h_RELU(dim_input)\n",
+    "opti_h = optim.Adam(h.parameters(), lr=lr_h,weight_decay=weight_decay)\n",
+    "\n",
+    "phi=NN_phi(dim_input)\n",
+    "opti_phi = optim.Adam(phi.parameters(), lr=lr_phi,maximize=True)\n",
+    "\n",
+    "strategy = WAR(X_train,y_train,X_test,y_test,idxs_lb,total_epoch_h,total_epoch_phi,batch_size_train,num_elem_queried,phi\n",
+    "                ,h,opti_phi,opti_h,second_query_strategy)\n",
+    "    \n",
+    "error_each_round,error_each_round_per,error_each_round_rmse,t1_descend_list,t2_ascend_list=full_training(\n",
+    "    strategy,num_round,show_losses,show_chosen_each_round\n",
+    "    ,reset_phi,reset_h,weight_decay,lr_h,lr_phi,reduced,eta)\n",
+    "\n",
+    "\n",
+    "stop=time.time()\n",
+    "\n",
+    "time_execution(start,stop)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot the loss of h\n",
+    "\n",
+    "plt.plot(list(itertools.chain(*t1_descend_list)),c=\"green\")\n",
+    "plt.grid(True)\n",
+    "plt.yscale(\"log\")\n",
+    "plt.title(\"T1 loss evolution each batch\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot the loss of phi\n",
+    "\n",
+    "plt.plot(np.array(list(itertools.chain(*t2_ascend_list))),c=\"brown\")\n",
+    "plt.grid(True)\n",
+    "plt.title(\"T2 loss evolution each batch\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "#plot RMSE\n",
+    "\n",
+    "plt.plot(error_each_round_rmse)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"RMSE of h each rounds\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot MAE\n",
+    "\n",
+    "plt.plot(error_each_round)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"mean absolute error of h each rounds\",fontsize=20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "#plot MAPE\n",
+    "\n",
+    "plt.plot(error_each_round_per)\n",
+    "plt.grid(True)\n",
+    "plt.title(\"mean absolute percentage error of h each rounds\",fontsize=20)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

WAR/Experiment_functions.py

@@ -0,0 +1,94 @@
+import matplotlib.pyplot as plt
+import seaborn as sns
+import time
+import numpy as np
+import pandas as pd
+from sklearn.decomposition import PCA
+import torch
+
+
+#Execution duration
+
+def time_execution(start,end):
+    timespan=end-start
+    minutes=timespan//60
+    secondes=timespan%60
+    heures=minutes//60
+    minutes=minutes%60
+    print(f"{int(heures)}h {int(minutes)} min {secondes} s")
+    return(f"{int(heures)}h {int(minutes)} min {secondes} s")
+
+
+#Graphs
+
+def display_prediction(X_test,h,y_test,rd):
+    plt.figure(figsize=[9,6])
+        
+    plt.scatter(X_test,h(X_test).cpu(),label="predicted values")
+    plt.scatter(X_test,y_test,label="true_values")
+    plt.legend()
+    if rd=="final":
+        plt.title("true et predicted values at the end")
+    else:plt.title(f"true et predicted values after {rd} rounds")
+    
+
+def display_chosen_labelled_datas_PCA(X_train,idx_lb,y_train,b_idxs,rd):
+    
+    pca = PCA(n_components=2)
+    transformed = pca.fit_transform(X=X_train)
+    x_component = transformed[:, 0]
+    
+    plt.figure(figsize=[9,6])
+    plt.scatter(transformed[:, 0][~idx_lb],transformed[:, 1][~idx_lb],label="unlabelled points",c="brown")
+    plt.scatter(transformed[:, 0][idx_lb],transformed[:, 1][idx_lb],label="labelled points")
+    plt.scatter(transformed[:, 0][b_idxs],transformed[:, 1][b_idxs],label="new points added",c="yellow")
+    plt.legend()
+    plt.title(f"points selected after {rd} rounds")
+    
+def display_chosen_labelled_datas(X_train,idx_lb,y_train,b_idxs,rd):
+    plt.figure(figsize=[9,6])
+    
+    plt.scatter(X_train[~idx_lb],y_train[~idx_lb],label="unlabelled points",c="brown")   
+    plt.scatter(X_train[idx_lb],y_train[idx_lb],label="labelled points")
+    plt.scatter(X_train[b_idxs],y_train[b_idxs],label="new points added",c="yellow")
+    plt.legend()
+    plt.title(f"points selected after {rd} rounds")
+        
+def display_loss_t1(t1_descend,rd):
+    plt.figure(figsize=[9,6])
+    plt.plot(t1_descend)
+    plt.xlabel("batch")
+    plt.title(f"t1 loss evolution each batch after {rd} rounds")
+    
+
+def display_loss_t2(t2_ascend,rd):
+    plt.figure(figsize=[9,6])
+    plt.plot(t2_ascend)
+    plt.xlabel("batch")
+    plt.title(f"t2 loss evolution each batch after {rd} rounds")
+    
+def display_phi(X_train,phi,rd=None):
+    plt.figure(figsize=[9,6])
+    plt.scatter(X_train,phi(X_train))
+    plt.xlabel("X_train")
+    plt.title(f"phi function on the full trainset after {rd} rounds")
+    
+
+#Metrics        
+        
+def MAPE(X_test,y_test,h):
+    acc_per_i=sum(abs(h(X_test)-y_test)/abs(y_test))
+    acc_per_i = acc_per_i[0]/len(y_test)
+    return acc_per_i    
+
+
+def MAE(X_test,y_test,h):
+    acc_i = sum(abs((h(X_test)-y_test)))
+    acc_i = acc_i[0]/len(y_test)
+    return acc_i
+
+def RMSE(X_test,y_test,h):
+    acc_i = ((h(X_test)-y_test)**2).mean()
+    return torch.sqrt(acc_i)  
+
+

WAR/Models.py

@@ -0,0 +1,57 @@
+import torch
+import torch.nn as nn
+import torchvision
+from monotonenorm import GroupSort, direct_norm
+
+
+# GroupSort Neural Networks created using monotonenorm package.
+#See https://github.com/niklasnolte/MonotoneNorm for more information
+
+class NN_phi(nn.Module):
+    
+    def __init__(self,dim_input):
+        
+        
+        super(NN_phi, self).__init__()
+        self.linear1=direct_norm(torch.nn.Linear(dim_input,16),kind="two-inf")
+        self.group1=GroupSort(16//2)#GroupSort with a grouping size of 2
+        self.linear2=direct_norm(torch.nn.Linear(16,32),kind="inf")
+        self.group2=GroupSort(32//2)
+        self.linear3=direct_norm(torch.nn.Linear(32,1),kind="inf")
+    
+
+    def forward(self, x):
+        x=self.linear1(x)
+        x=self.group1(x)
+        x=self.linear2(x)
+        x=self.group2(x)
+        x=self.linear3(x)
+        
+        return x
+    
+
+
+class NN_h_RELU(nn.Module):
+    def __init__(self,dim_input):
+        
+        
+        super(NN_h_RELU, self).__init__()
+        
+        
+        self.linear1=torch.nn.Linear(dim_input,16)
+        self.RELU=torch.nn.ReLU()
+        self.linear2=torch.nn.Linear(16,32)
+        self.linear3=torch.nn.Linear(32,1)
+            
+            
+            
+
+    def forward(self, x):
+        x=self.linear1(x)
+        x=self.RELU(x)
+        x=self.linear2(x)
+        x=self.RELU(x)
+        x=self.linear3(x)
+            
+        return x
+    

WAR/dataset_handler.py

@@ -0,0 +1,124 @@
+import torch
+from torch.utils.data import Dataset
+import random
+import numpy as np
+import pandas as pd
+
+from sklearn.model_selection import train_test_split
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import MinMaxScaler
+from sklearn import datasets
+
+
+def import_dataset(name):# import dataset among a list a available ones 
+    
+    if name=="boston":
+        data_url = "http://lib.stat.cmu.edu/datasets/boston"
+        raw_df = pd.read_csv(data_url, sep="\s+", skiprows=22, header=None)
+        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
+        target = raw_df.values[1::2, 2]
+        y_boston=target
+        X_boston=data
+        y_boston=torch.Tensor(y_boston).view(len(y_boston),1).float()
+        X_boston=torch.Tensor(X_boston).float()
+        return X_boston,y_boston
+    
+    if name=="airfoil":
+        columns_names=["Frequency","Angle of attack","Chord length","Free-stream velocity","Suction side displacement thickness","sound pressure level"]
+        airfoil=pd.read_csv('datasets/airfoil_self_noise.dat',sep='\t',names=columns_names)
+        y_airfoil=airfoil["sound pressure level"]
+        X_airfoil=airfoil.drop("sound pressure level",axis=1)
+        y_airfoil=torch.Tensor(y_airfoil).view(len(y_airfoil),1).float()
+        X_airfoil=torch.Tensor(X_airfoil.values).float()
+        return X_airfoil,y_airfoil
+    
+    if name=="energy1":
+        energy=pd.read_csv('datasets/energy efficiency.csv')
+        y_energy=energy["Y1"]
+        X_energy=energy.drop(["Y2","Y1"],axis=1)
+        y_energy=torch.Tensor(y_energy).view(len(y_energy),1).float()
+        X_energy=torch.Tensor(X_energy.values).float()
+        return X_energy,y_energy
+    
+    if name=="energy2":# other target function
+        energy=pd.read_csv('datasets/energy efficiency.csv')
+        y_energy=energy["Y2"]
+        X_energy=energy.drop(["Y2","Y1"],axis=1)
+        y_energy=torch.Tensor(y_energy).view(len(y_energy),1).float()
+        X_energy=torch.Tensor(X_energy.values).float()
+        return X_energy,y_energy
+    
+    if name=="yacht":        
+        yacht=pd.read_csv('datasets/yacht_hydrodynamics.data',sep=' ',header=None)
+        y_yacht=yacht[6]
+        X_yacht=yacht.drop([6],axis=1)
+        y_yacht=torch.Tensor(y_yacht).view(len(y_yacht),1).float()
+        X_yacht=torch.Tensor(X_yacht.values).float()
+        return X_yacht,y_yacht  
+    
+    if name=="concrete_slump":        
+        concrete=pd.read_csv('datasets/slump_test.data',sep=',')
+        y_concrete=concrete["SLUMP(cm)"]
+        X_concrete=concrete.drop(["No","SLUMP(cm)","FLOW(cm)","Compressive Strength (28-day)(Mpa)"],axis=1)
+        y_concrete=torch.Tensor(y_concrete).view(len(y_concrete),1).float()
+        X_concrete=torch.Tensor(X_concrete.values).float()
+        return X_concrete,y_concrete 
+    
+    if name=="concrete_flow":#other target function
+        concrete=pd.read_csv('datasets/slump_test.data',sep=',')
+        y_concrete=concrete["FLOW(cm)"]
+        X_concrete=concrete.drop(["No","FLOW(cm)","SLUMP(cm)","Compressive Strength (28-day)(Mpa)"],axis=1)
+        y_concrete=torch.Tensor(y_concrete).view(len(y_concrete),1).float()
+        X_concrete=torch.Tensor(X_concrete.values).float()
+        return X_concrete,y_concrete 
+    
+    if name=="concrete_compressive":#other target function
+        concrete=pd.read_csv('datasets/slump_test.data',sep=',')
+        y_concrete=concrete["Compressive Strength (28-day)(Mpa)"]
+        X_concrete=concrete.drop(["No","FLOW(cm)","SLUMP(cm)","Compressive Strength (28-day)(Mpa)"],axis=1)
+        y_concrete=torch.Tensor(y_concrete).view(len(y_concrete),1).float()
+        X_concrete=torch.Tensor(X_concrete.values).float()
+        return X_concrete,y_concrete 
+    if name=="x_squared":
+        
+        data_generated=100
+        x_b=torch.tensor([random.random() for i in range(data_generated)])
+        x_carré_b=x_b.view(x_b.size()[0],1)
+        y_carré_b=(x_b**2 + torch.tensor([np.random.normal(loc=0,scale=0.05) for i in range(data_generated)])).view(x_b.size()[0],1)
+        return x_carré_b,y_carré_b
+    
+    if name=="news_popularity":
+        news=pd.read_csv('datasets/OnlineNewsPopularity/OnlineNewsPopularity.csv')
+        y_news=news[" shares"]
+        X_news=news.drop([" shares","url"," timedelta"],axis=1)
+        y_news=torch.Tensor(y_news).view(len(y_news),1).float()
+        X_news=torch.Tensor(X_news.values).float()
+        return X_news,y_news
+
+def get_dataset(proportion=0.2,dataset="boston"):# scale and process the data
+
+    scaler = MinMaxScaler()
+    X,y=import_dataset(dataset)
+    X=torch.Tensor(scaler.fit_transform(X))
+    X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=proportion)
+    print(f"Shape of the training set: {X_train.shape}")
+    return X_train,X_test,y_train,y_test
+
+
+
+class myData(Dataset):
+    
+    def __init__(self,x,y):
+        self.x=x
+        self.y=y
+        self.shape=x.size(0)
+        
+    def __getitem__(self,index):
+        return self.x[index],self.y[index]
+    
+    def __len__(self):
+        return self.shape
+    
+
+
+

WAR/full_training_process.py

@@ -0,0 +1,116 @@
+import numpy as np
+import warnings
+import torch.optim as optim
+import torch
+from WAR.Models import NN_phi,NN_h_RELU
+from WAR.Experiment_functions import *
+
+
+
+def full_training(strategy,num_round,show_losses,show_chosen_each_round,
+                  reset_phi,reset_h,weight_decay,lr_h=None,lr_phi=None,reduced=False,eta=1
+                 ):
+    
+    """
+    strategy: an object of class WAR
+    num_round: total number of query rounds
+    show_losses: display graphs showing the loss of h and phi each rounds
+    show_chosen_each_round:display a graph showing the data queried each round
+    reset_phi: if True, the phi neural network is reset after each round. can avoir overfitting but increase the number of epochs required to train the model
+    reset_h:if True, the h neural network is reset after each round. can avoir overfitting but increase the number of epochs required to train the model
+    lr_h: learning rate of h
+    lr_phi: learning rate of phi
+    reduced: will divide each query criterion by their standard deviation. In the case where they don't have the same amplitude, This will give them the same weight in the querying process. Irrelevant parameter if there is only one query criterion 
+    eta:factor used to rebalance the criteria. If >1, distribution matching criterion gets more weight than the other(s). Irrelevant parameter if there is only one query criterion.
+    
+    """
+    t1_descend_list=[]
+    t2_ascend_list=[]
+    acc = []# MAE
+    acc_percentage=[] #MAPE
+    acc_rmse=[] #RMSE
+    
+    only_train=False
+    
+    for rd in range(1,num_round+1):
+
+        print('\n================Round {:d}==============='.format(rd))
+        
+        # if not enough unlabelled data to query a full batch, we will query the remaining data
+        if len(np.arange(strategy.n_pool)[~strategy.idx_lb])<=strategy.num_elem_queried:
+            only_train=True
+        
+        #reset neural networks
+        if reset_phi==True:
+            strategy.phi=NN_phi(dim_input=strategy.X_train.shape[1])
+            strategy.opti_phi = optim.Adam(strategy.phi.parameters(), lr=lr_phi,maximize=True)
+
+
+        if reset_h==True:
+            strategy.h=NN_h_RELU(dim_input=strategy.X_train.shape[1])
+            strategy.opti_h = optim.Adam(strategy.h.parameters(), lr=lr_h,weight_decay=weight_decay)
+
+        
+        t1,t2,b_idxs=strategy.train(only_train,reduced,eta)
+
+        
+        t1_descend_list.append(t1)
+        t2_ascend_list.append(t2)
+        if only_train==True:
+            strategy.idx_lb[:]= True
+        else:
+            
+            strategy.idx_lb[b_idxs] = True #"simulation" of the oracle who label the queried samples
+            
+        with torch.no_grad():
+            if show_losses:
+                display_loss_t1(t1,rd)
+                display_loss_t2(t2,rd)
+
+            if show_chosen_each_round:
+                if strategy.X_train.shape[1]==1:
+                    #display_phi(strategy.X_train,strategy.phi,rd)
+                    display_chosen_labelled_datas(strategy.X_train.cpu(),strategy.idx_lb,strategy.y_train.cpu(),b_idxs,rd)
+                    #display_prediction(strategy.X_test,strategy.h,strategy.y_test,rd)
+                    
+                else:
+                    display_chosen_labelled_datas_PCA(strategy.X_train.cpu(),strategy.idx_lb,strategy.y_train.cpu(),b_idxs,rd)
+
+
+            acc_rmse.append(RMSE(strategy.X_test,strategy.y_test,strategy.h).cpu())   
+            acc.append(MAE(strategy.X_test,strategy.y_test,strategy.h).cpu())
+            acc_percentage.append(MAPE(strategy.X_test,strategy.y_test,strategy.h).cpu())
+        
+            
+    print('\n================Final training===============')
+    
+
+    
+    t1,t2,_=strategy.train(only_train,reduced,eta)
+    
+    t1_descend_list.append(t1)
+    t2_ascend_list.append(t2)
+    
+    with torch.no_grad():
+        #display_loss_t1(t1,rd)
+        #display_prediction(strategy.X_test,strategy.h,strategy.y_test,"final")
+
+        acc.append(MAE(strategy.X_test,strategy.y_test,strategy.h).cpu())      
+        acc_percentage.append(MAPE(strategy.X_test,strategy.y_test,strategy.h).cpu())        
+        acc_rmse.append(RMSE(strategy.X_test,strategy.y_test,strategy.h).cpu()) 
+        
+    
+    return acc,acc_percentage, acc_rmse,t1_descend_list,t2_ascend_list
+
+
+
+
+
+
+def check_num_round(num_round,len_dataset,nb_initial_labelled_datas,num_elem_queried):
+    max_round=int(np.ceil((len_dataset-nb_initial_labelled_datas)/num_elem_queried))
+    if num_round>max_round:
+        warnings.warn(f"when querying {num_elem_queried} data per round, num_rounds={num_round} is exceeding"+
+        f" the maximum number of rounds (total data queried superior to number of initial unlabelled data).\nnum_round set to {max_round}")
+        num_round=max_round
+    return num_round

WAR/training_and_query.py

@@ -0,0 +1,226 @@
+import numpy as np
+import torch
+import torch.optim as optim
+from torch.utils.data import DataLoader
+import itertools
+
+from WAR.Experiment_functions import display_phi
+from WAR.dataset_handler import myData
+  
+        
+class WAR:
+
+    def __init__(self,X_train,y_train,X_test,y_test,idx_lb,total_epoch_h,total_epoch_phi,batch_size_train,num_elem_queried
+                 ,phi,h,opti_phi,opti_h,second_query_strategy=None):
+        
+        """
+        device: device on which to train the model.
+        X_train: trainset.
+        Y_train: labels of the trainset
+        idx_lb: indices of the trainset that would be considered as labelled.
+        n_pool: length of the trainset.
+        total_epoch_h: number of epochs to train h.
+        total_epoch_phi: number of epochs to train phi.
+        batch_size_train: size of the batch in the training process.
+        num_elem_queried: number of elem queried each round.
+        phi: phi neural network.
+        h: h neural network.
+        opti_phi: phi optimizer.
+        opti_h: h optimizer.
+        cost: define the cost function for both neural network. "MSE" or MAE".
+        second_query_strategy: second strategy to assist our distribution-matching criterion.
+        """
+        
+        self.device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.X_train = X_train.to(self.device)
+        self.y_train = y_train.to(self.device)
+        self.X_test=X_test.to(self.device)
+        self.y_test=y_test.to(self.device)
+        self.idx_lb  = idx_lb
+        self.n_pool  = len(y_train)
+        self.total_epoch_h=total_epoch_h
+        self.total_epoch_phi=total_epoch_phi
+        self.batch_size_train=batch_size_train
+        self.num_elem_queried=num_elem_queried
+        self.phi=phi.to(self.device)
+        self.h=h.to(self.device)
+        self.opti_phi=opti_phi
+        self.opti_h=opti_h
+        self.cost="MSE"
+        self.second_query_strategy=second_query_strategy
+        
+        
+        
+        
+    #cost function used to train both phi and h
+    def cost_func(self,predicted,true):
+        if self.cost=="MSE":
+            return (predicted-true)**2
+        elif self.cost=="MAE":
+            return abs(predicted-true)
+        else:
+            raise Exception("invalid cost function")
+      
+   
+
+    
+    def train(self,only_train=False,reduced=True,eta=3):# train function for one round
+        
+        """
+        only_train: activite when there is no more unlabelled data in the trainset. Will only train h and not train phi or query data.
+        reduced: will divide each query criterion by their standard deviation. In the case where they don't have the same amplitude, This will give them the same weight in the querying process. Irrelevant parameter if there is only one query criterion (self.second_query_strategy=None).
+        eta:factor used to rebalance the criteria. If >1, distribution matching criterion gets more weight than the other(s). Irrelevant parameter if there is only one query criterion.
+        
+        """
+        #recover loss 
+        t1_descend=[]
+        t2_ascend=[]
+        
+        # separating labelled and unlabelled data respectively
+        idx_lb_train = np.arange(self.n_pool)[self.idx_lb]
+        idx_ulb_train = np.arange(self.n_pool)[~self.idx_lb]
+        
+        
+        trainset_labelled=myData(self.X_train[idx_lb_train],self.y_train[idx_lb_train])
+        trainloader_labelled= DataLoader(trainset_labelled,shuffle=True,batch_size=self.batch_size_train)
+
+        for epoch in range(self.total_epoch_h):
+      
+            for i,data in enumerate(trainloader_labelled,0):
+                label_x, label_y=data
+                self.opti_h.zero_grad()                 
+                # T1 (train h)
+                lb_out = self.h(label_x)
+                h_descent=torch.mean(self.cost_func(lb_out,label_y))
+                t1_descend.append(h_descent.detach().cpu())
+                h_descent.backward()
+                self.opti_h.step()
+                
+              
+        b_idxs=[]# batch of queried points             
+        if not only_train: 
+            #T2 (train phi) 
+                                 
+            # temporary set of labelled data indices. Used only to retrain phi during the time oracle has not been called. 
+            #h is no retrained during this time.
+            idxs_temp=self.idx_lb.copy()
+            
+            for elem_queried in range(self.num_elem_queried):
+                
+                trainset_total=myData(self.X_train,self.y_train)
+                trainloader_total= DataLoader(trainset_total,shuffle=True,batch_size=len(trainset_total))
+                trainset_labelled=myData(self.X_train[idx_lb_train],self.y_train[idx_lb_train])
+                trainloader_labelled= DataLoader(trainset_labelled,shuffle=True,batch_size=self.batch_size_train)
+                for epoch in range(self.total_epoch_phi):  
+                    iterator_total_phi=itertools.cycle(trainloader_total)
+                    iterator_labelled_phi=itertools.cycle(trainloader_labelled)
+                    for i in range(len(trainloader_labelled)):
+                        label_x,label_y = next(iterator_labelled_phi)
+                        total_x,total_y = next(iterator_total_phi)
+                        #display_phi(self.X_train,self.phi)
+                        self.opti_phi.zero_grad()
+                        phi_ascent = (torch.mean(self.phi(total_x))-torch.mean(self.phi(label_x)))
+                        t2_ascend.append(phi_ascent.detach().cpu())
+                        phi_ascent.backward()
+                        self.opti_phi.step()
+                        
+                # Query process
+                b_queried=self.query(reduced,eta,idx_ulb_train)# query one element
+                idxs_temp[b_queried]=True #add it to the temporary set of labeled point indices indices
+                idx_ulb_train = np.arange(self.n_pool)[~idxs_temp] #update the set of unlabeled point indices
+                idx_lb_train = np.arange(self.n_pool)[idxs_temp] #update the set of labeled point indices
+                b_idxs.append(b_queried)#add the chosen point in the batch
+            self.idx_lb=idxs_temp#end of the query process: update the true set of labeled point indices indices
+        return t1_descend,t2_ascend,b_idxs
+
+    
+    
+    def query(self,reduced,eta,idx_ulb_train):# computing T3: query one point according to the chosen query criteria
+         
+                          
+        """
+        reduced:same as for function "train"
+        eta: sme as for function "train"
+        idx_ulb_train:indices of unlabeled points
+        
+        """
+
+        if self.second_query_strategy=="loss_approximation":
+            second_query_criterion = self.predict_loss(self.X_train[idx_ulb_train])
+                          
+        with torch.no_grad():
+            phi_scores =  self.phi(self.X_train[idx_ulb_train]).view(-1)
+
+            if reduced and self.second_query_strategy!=None:
+                phi_scores_reduced=phi_scores/torch.std(phi_scores)
+                second_query_criterion_reduced=second_query_criterion/torch.std(second_query_criterion)
+                total_scores =-(eta*phi_scores_reduced+second_query_criterion_reduced )  
+                          
+            elif self.second_query_strategy!=None:
+                total_scores =-(eta*phi_scores+second_query_criterion)
+                          
+            else:
+                total_scores =-eta*phi_scores
+                
+            b=torch.argmin(total_scores)
+        
+        return idx_ulb_train[b]
+    
+                          
+    def predict_loss(self,X):# Second query criterion which act as loss estimator (uncertainty and diversity-based sampling)
+        
+        """
+        X: set of unlabeled elements of the trainset
+        
+        """
+                          
+        idxs_lb=np.arange(self.n_pool)[self.idx_lb]#get labeled data indices
+        losses=[]
+        with torch.no_grad():
+            for i in X:
+                idx_nearest_Xk,dist=self.Idx_NearestP(i,idxs_lb) 
+                losses.append(self.Max_cost_B(idx_nearest_Xk,dist,i))
+        
+        return torch.Tensor(losses).to(self.device)
+    
+    def Idx_NearestP(self,Xu,idxs_lb):# Return the closest labeled point to the unlabeled point 
+        
+                          
+        """
+        Xu:unlabeled point
+        idxs_lb: indices of labeled points
+        
+        """
+                          
+        distances=[]
+        for i in idxs_lb:
+            distances.append(torch.norm(Xu-self.X_train[i]))
+            
+        return idxs_lb[distances.index(min(distances))],float(min(distances))
+    
+
+    
+    def Max_cost_B(self,idx_Xk,distance,i):#return the "maximum loss" of the unlabeled point
+       
+        """
+        idx_Xk: labeled point indice nearest to the unlabeled point
+        distance: distance between them
+        i:unlabeled point
+        
+        """
+                          
+        est_h_unl_X=self.h(i)
+        true_value_labelled_X=self.y_train[idx_Xk]
+        bound_min= true_value_labelled_X-distance
+        bound_max= true_value_labelled_X+distance
+        return max(self.cost_func(est_h_unl_X,bound_min),self.cost_func(est_h_unl_X,bound_max))[0]
+    
+        
+
+    
+    
+            
+
+    
+    
+    

datasets/OnlineNewsPopularity/OnlineNewsPopularity.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:011c4f45de56844f346f232dc455ce6fc88a7c610ac57ad16f31c6b19c2ca435
+size 16874641

datasets/OnlineNewsPopularity/OnlineNewsPopularity.names

@@ -0,0 +1,194 @@
+1. Title: Online News Popularity
+
+2. Source Information
+    -- Creators: Kelwin Fernandes (kafc ‘@’ inesctec.pt, kelwinfc ’@’ gmail.com),
+                 Pedro Vinagre (pedro.vinagre.sousa ’@’ gmail.com) and
+                 Pedro Sernadela
+   -- Donor: Kelwin Fernandes (kafc ’@’ inesctec.pt, kelwinfc '@' gmail.com)
+   -- Date: May, 2015
+
+3. Past Usage:
+    1. K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision
+       Support System for Predicting the Popularity of Online News. Proceedings
+       of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence,
+       September, Coimbra, Portugal.
+
+       -- Results: 
+          -- Binary classification as popular vs unpopular using a decision
+             threshold of 1400 social interactions.
+          -- Experiments with different models: Random Forest (best model),
+             Adaboost, SVM, KNN and Naïve Bayes.
+          -- Recorded 67% of accuracy and 0.73 of AUC.
+    - Predicted attribute: online news popularity (boolean)
+
+4. Relevant Information:
+   -- The articles were published by Mashable (www.mashable.com) and their
+      content as the rights to reproduce it belongs to them. Hence, this
+      dataset does not share the original content but some statistics
+      associated with it. The original content be publicly accessed and
+      retrieved using the provided urls.
+   -- Acquisition date: January 8, 2015
+   -- The estimated relative performance values were estimated by the authors
+      using a Random Forest classifier and a rolling windows as assessment
+      method.  See their article for more details on how the relative
+      performance values were set.
+
+5. Number of Instances: 39797 
+
+6. Number of Attributes: 61 (58 predictive attributes, 2 non-predictive, 
+                             1 goal field)
+
+7. Attribute Information:
+     0. url:                           URL of the article
+     1. timedelta:                     Days between the article publication and
+                                       the dataset acquisition
+     2. n_tokens_title:                Number of words in the title
+     3. n_tokens_content:              Number of words in the content
+     4. n_unique_tokens:               Rate of unique words in the content
+     5. n_non_stop_words:              Rate of non-stop words in the content
+     6. n_non_stop_unique_tokens:      Rate of unique non-stop words in the
+                                       content
+     7. num_hrefs:                     Number of links
+     8. num_self_hrefs:                Number of links to other articles
+                                       published by Mashable
+     9. num_imgs:                      Number of images
+    10. num_videos:                    Number of videos
+    11. average_token_length:          Average length of the words in the
+                                       content
+    12. num_keywords:                  Number of keywords in the metadata
+    13. data_channel_is_lifestyle:     Is data channel 'Lifestyle'?
+    14. data_channel_is_entertainment: Is data channel 'Entertainment'?
+    15. data_channel_is_bus:           Is data channel 'Business'?
+    16. data_channel_is_socmed:        Is data channel 'Social Media'?
+    17. data_channel_is_tech:          Is data channel 'Tech'?
+    18. data_channel_is_world:         Is data channel 'World'?
+    19. kw_min_min:                    Worst keyword (min. shares)
+    20. kw_max_min:                    Worst keyword (max. shares)
+    21. kw_avg_min:                    Worst keyword (avg. shares)
+    22. kw_min_max:                    Best keyword (min. shares)
+    23. kw_max_max:                    Best keyword (max. shares)
+    24. kw_avg_max:                    Best keyword (avg. shares)
+    25. kw_min_avg:                    Avg. keyword (min. shares)
+    26. kw_max_avg:                    Avg. keyword (max. shares)
+    27. kw_avg_avg:                    Avg. keyword (avg. shares)
+    28. self_reference_min_shares:     Min. shares of referenced articles in
+                                       Mashable
+    29. self_reference_max_shares:     Max. shares of referenced articles in
+                                       Mashable
+    30. self_reference_avg_sharess:    Avg. shares of referenced articles in
+                                       Mashable
+    31. weekday_is_monday:             Was the article published on a Monday?
+    32. weekday_is_tuesday:            Was the article published on a Tuesday?
+    33. weekday_is_wednesday:          Was the article published on a Wednesday?
+    34. weekday_is_thursday:           Was the article published on a Thursday?
+    35. weekday_is_friday:             Was the article published on a Friday?
+    36. weekday_is_saturday:           Was the article published on a Saturday?
+    37. weekday_is_sunday:             Was the article published on a Sunday?
+    38. is_weekend:                    Was the article published on the weekend?
+    39. LDA_00:                        Closeness to LDA topic 0
+    40. LDA_01:                        Closeness to LDA topic 1
+    41. LDA_02:                        Closeness to LDA topic 2
+    42. LDA_03:                        Closeness to LDA topic 3
+    43. LDA_04:                        Closeness to LDA topic 4
+    44. global_subjectivity:           Text subjectivity
+    45. global_sentiment_polarity:     Text sentiment polarity
+    46. global_rate_positive_words:    Rate of positive words in the content
+    47. global_rate_negative_words:    Rate of negative words in the content
+    48. rate_positive_words:           Rate of positive words among non-neutral
+                                       tokens
+    49. rate_negative_words:           Rate of negative words among non-neutral
+                                       tokens
+    50. avg_positive_polarity:         Avg. polarity of positive words
+    51. min_positive_polarity:         Min. polarity of positive words
+    52. max_positive_polarity:         Max. polarity of positive words
+    53. avg_negative_polarity:         Avg. polarity of negative  words
+    54. min_negative_polarity:         Min. polarity of negative  words
+    55. max_negative_polarity:         Max. polarity of negative  words
+    56. title_subjectivity:            Title subjectivity
+    57. title_sentiment_polarity:      Title polarity
+    58. abs_title_subjectivity:        Absolute subjectivity level
+    59. abs_title_sentiment_polarity:  Absolute polarity level
+    60. shares:                        Number of shares (target)
+
+8. Missing Attribute Values: None
+
+9. Class Distribution: the class value (shares) is continuously valued. We
+                       transformed the task into a binary task using a decision
+                       threshold of 1400.
+
+   Shares Value Range:   Number of Instances in Range:
+   <  1400            18490
+   >= 1400            21154
+
+
+Summary Statistics:
+                       Feature       Min          Max         Mean           SD
+                     timedelta    8.0000     731.0000     354.5305     214.1611
+                n_tokens_title    2.0000      23.0000      10.3987       2.1140
+              n_tokens_content    0.0000    8474.0000     546.5147     471.1016
+               n_unique_tokens    0.0000     701.0000       0.5482       3.5207
+              n_non_stop_words    0.0000    1042.0000       0.9965       5.2312
+      n_non_stop_unique_tokens    0.0000     650.0000       0.6892       3.2648
+                     num_hrefs    0.0000     304.0000      10.8837      11.3319
+                num_self_hrefs    0.0000     116.0000       3.2936       3.8551
+                      num_imgs    0.0000     128.0000       4.5441       8.3093
+                    num_videos    0.0000      91.0000       1.2499       4.1078
+          average_token_length    0.0000       8.0415       4.5482       0.8444
+                  num_keywords    1.0000      10.0000       7.2238       1.9091
+     data_channel_is_lifestyle    0.0000       1.0000       0.0529       0.2239
+ data_channel_is_entertainment    0.0000       1.0000       0.1780       0.3825
+           data_channel_is_bus    0.0000       1.0000       0.1579       0.3646
+        data_channel_is_socmed    0.0000       1.0000       0.0586       0.2349
+          data_channel_is_tech    0.0000       1.0000       0.1853       0.3885
+         data_channel_is_world    0.0000       1.0000       0.2126       0.4091
+                    kw_min_min   -1.0000     377.0000      26.1068      69.6323
+                    kw_max_min    0.0000  298400.0000    1153.9517    3857.9422
+                    kw_avg_min   -1.0000   42827.8571     312.3670     620.7761
+                    kw_min_max    0.0000  843300.0000   13612.3541   57985.2980
+                    kw_max_max    0.0000  843300.0000  752324.0667  214499.4242
+                    kw_avg_max    0.0000  843300.0000  259281.9381  135100.5433
+                    kw_min_avg   -1.0000    3613.0398    1117.1466    1137.4426
+                    kw_max_avg    0.0000  298400.0000    5657.2112    6098.7950
+                    kw_avg_avg    0.0000   43567.6599    3135.8586    1318.1338
+     self_reference_min_shares    0.0000  843300.0000    3998.7554   19738.4216
+     self_reference_max_shares    0.0000  843300.0000   10329.2127   41027.0592
+    self_reference_avg_sharess    0.0000  843300.0000    6401.6976   24211.0269
+             weekday_is_monday    0.0000       1.0000       0.1680       0.3739
+            weekday_is_tuesday    0.0000       1.0000       0.1864       0.3894
+          weekday_is_wednesday    0.0000       1.0000       0.1875       0.3903
+           weekday_is_thursday    0.0000       1.0000       0.1833       0.3869
+             weekday_is_friday    0.0000       1.0000       0.1438       0.3509
+           weekday_is_saturday    0.0000       1.0000       0.0619       0.2409
+             weekday_is_sunday    0.0000       1.0000       0.0690       0.2535
+                    is_weekend    0.0000       1.0000       0.1309       0.3373
+                        LDA_00    0.0000       0.9270       0.1846       0.2630
+                        LDA_01    0.0000       0.9259       0.1413       0.2197
+                        LDA_02    0.0000       0.9200       0.2163       0.2821
+                        LDA_03    0.0000       0.9265       0.2238       0.2952
+                        LDA_04    0.0000       0.9272       0.2340       0.2892
+           global_subjectivity    0.0000       1.0000       0.4434       0.1167
+     global_sentiment_polarity   -0.3937       0.7278       0.1193       0.0969
+    global_rate_positive_words    0.0000       0.1555       0.0396       0.0174
+    global_rate_negative_words    0.0000       0.1849       0.0166       0.0108
+           rate_positive_words    0.0000       1.0000       0.6822       0.1902
+           rate_negative_words    0.0000       1.0000       0.2879       0.1562
+         avg_positive_polarity    0.0000       1.0000       0.3538       0.1045
+         min_positive_polarity    0.0000       1.0000       0.0954       0.0713
+         max_positive_polarity    0.0000       1.0000       0.7567       0.2478
+         avg_negative_polarity   -1.0000       0.0000      -0.2595       0.1277
+         min_negative_polarity   -1.0000       0.0000      -0.5219       0.2903
+         max_negative_polarity   -1.0000       0.0000      -0.1075       0.0954
+            title_subjectivity    0.0000       1.0000       0.2824       0.3242
+      title_sentiment_polarity   -1.0000       1.0000       0.0714       0.2654
+        abs_title_subjectivity    0.0000       0.5000       0.3418       0.1888
+  abs_title_sentiment_polarity    0.0000       1.0000       0.1561       0.2263
+
+   
+ Citation Request:
+ 
+ Please include this citation if you plan to use this database: 
+ 
+    K. Fernandes, P. Vinagre and P. Cortez. A Proactive Intelligent Decision
+    Support System for Predicting the Popularity of Online News. Proceedings
+    of the 17th EPIA 2015 - Portuguese Conference on Artificial Intelligence,
+    September, Coimbra, Portugal.

datasets/airfoil_self_noise.dat

@@ -0,0 +1,1503 @@
+800	0	0.3048	71.3	0.00266337	126.201
+1000	0	0.3048	71.3	0.00266337	125.201
+1250	0	0.3048	71.3	0.00266337	125.951
+1600	0	0.3048	71.3	0.00266337	127.591
+2000	0	0.3048	71.3	0.00266337	127.461
+2500	0	0.3048	71.3	0.00266337	125.571
+3150	0	0.3048	71.3	0.00266337	125.201
+4000	0	0.3048	71.3	0.00266337	123.061
+5000	0	0.3048	71.3	0.00266337	121.301
+6300	0	0.3048	71.3	0.00266337	119.541
+8000	0	0.3048	71.3	0.00266337	117.151
+10000	0	0.3048	71.3	0.00266337	115.391
+12500	0	0.3048	71.3	0.00266337	112.241
+16000	0	0.3048	71.3	0.00266337	108.721
+500	0	0.3048	55.5	0.00283081	126.416
+630	0	0.3048	55.5	0.00283081	127.696
+800	0	0.3048	55.5	0.00283081	128.086
+1000	0	0.3048	55.5	0.00283081	126.966
+1250	0	0.3048	55.5	0.00283081	126.086
+1600	0	0.3048	55.5	0.00283081	126.986
+2000	0	0.3048	55.5	0.00283081	126.616
+2500	0	0.3048	55.5	0.00283081	124.106
+3150	0	0.3048	55.5	0.00283081	123.236
+4000	0	0.3048	55.5	0.00283081	121.106
+5000	0	0.3048	55.5	0.00283081	119.606
+6300	0	0.3048	55.5	0.00283081	117.976
+8000	0	0.3048	55.5	0.00283081	116.476
+10000	0	0.3048	55.5	0.00283081	113.076
+12500	0	0.3048	55.5	0.00283081	111.076
+200	0	0.3048	39.6	0.00310138	118.129
+250	0	0.3048	39.6	0.00310138	119.319
+315	0	0.3048	39.6	0.00310138	122.779
+400	0	0.3048	39.6	0.00310138	124.809
+500	0	0.3048	39.6	0.00310138	126.959
+630	0	0.3048	39.6	0.00310138	128.629
+800	0	0.3048	39.6	0.00310138	129.099
+1000	0	0.3048	39.6	0.00310138	127.899
+1250	0	0.3048	39.6	0.00310138	125.499
+1600	0	0.3048	39.6	0.00310138	124.049
+2000	0	0.3048	39.6	0.00310138	123.689
+2500	0	0.3048	39.6	0.00310138	121.399
+3150	0	0.3048	39.6	0.00310138	120.319
+4000	0	0.3048	39.6	0.00310138	119.229
+5000	0	0.3048	39.6	0.00310138	117.789
+6300	0	0.3048	39.6	0.00310138	116.229
+8000	0	0.3048	39.6	0.00310138	114.779
+10000	0	0.3048	39.6	0.00310138	112.139
+12500	0	0.3048	39.6	0.00310138	109.619
+200	0	0.3048	31.7	0.00331266	117.195
+250	0	0.3048	31.7	0.00331266	118.595
+315	0	0.3048	31.7	0.00331266	122.765
+400	0	0.3048	31.7	0.00331266	125.045
+500	0	0.3048	31.7	0.00331266	127.315
+630	0	0.3048	31.7	0.00331266	129.095
+800	0	0.3048	31.7	0.00331266	129.235
+1000	0	0.3048	31.7	0.00331266	127.365
+1250	0	0.3048	31.7	0.00331266	124.355
+1600	0	0.3048	31.7	0.00331266	122.365
+2000	0	0.3048	31.7	0.00331266	122.375
+2500	0	0.3048	31.7	0.00331266	120.755
+3150	0	0.3048	31.7	0.00331266	119.135
+4000	0	0.3048	31.7	0.00331266	118.145
+5000	0	0.3048	31.7	0.00331266	115.645
+6300	0	0.3048	31.7	0.00331266	113.775
+8000	0	0.3048	31.7	0.00331266	110.515
+10000	0	0.3048	31.7	0.00331266	108.265
+800	1.5	0.3048	71.3	0.00336729	127.122
+1000	1.5	0.3048	71.3	0.00336729	125.992
+1250	1.5	0.3048	71.3	0.00336729	125.872
+1600	1.5	0.3048	71.3	0.00336729	126.632
+2000	1.5	0.3048	71.3	0.00336729	126.642
+2500	1.5	0.3048	71.3	0.00336729	124.512
+3150	1.5	0.3048	71.3	0.00336729	123.392
+4000	1.5	0.3048	71.3	0.00336729	121.762
+5000	1.5	0.3048	71.3	0.00336729	119.632
+6300	1.5	0.3048	71.3	0.00336729	118.122
+8000	1.5	0.3048	71.3	0.00336729	115.372
+10000	1.5	0.3048	71.3	0.00336729	113.492
+12500	1.5	0.3048	71.3	0.00336729	109.222
+16000	1.5	0.3048	71.3	0.00336729	106.582
+315	1.5	0.3048	39.6	0.00392107	121.851
+400	1.5	0.3048	39.6	0.00392107	124.001
+500	1.5	0.3048	39.6	0.00392107	126.661
+630	1.5	0.3048	39.6	0.00392107	128.311
+800	1.5	0.3048	39.6	0.00392107	128.831
+1000	1.5	0.3048	39.6	0.00392107	127.581
+1250	1.5	0.3048	39.6	0.00392107	125.211
+1600	1.5	0.3048	39.6	0.00392107	122.211
+2000	1.5	0.3048	39.6	0.00392107	122.101
+2500	1.5	0.3048	39.6	0.00392107	120.981
+3150	1.5	0.3048	39.6	0.00392107	119.111
+4000	1.5	0.3048	39.6	0.00392107	117.741
+5000	1.5	0.3048	39.6	0.00392107	116.241
+6300	1.5	0.3048	39.6	0.00392107	114.751
+8000	1.5	0.3048	39.6	0.00392107	112.251
+10000	1.5	0.3048	39.6	0.00392107	108.991
+12500	1.5	0.3048	39.6	0.00392107	106.111
+400	3	0.3048	71.3	0.00425727	127.564
+500	3	0.3048	71.3	0.00425727	128.454
+630	3	0.3048	71.3	0.00425727	129.354
+800	3	0.3048	71.3	0.00425727	129.494
+1000	3	0.3048	71.3	0.00425727	129.004
+1250	3	0.3048	71.3	0.00425727	127.634
+1600	3	0.3048	71.3	0.00425727	126.514
+2000	3	0.3048	71.3	0.00425727	125.524
+2500	3	0.3048	71.3	0.00425727	124.024
+3150	3	0.3048	71.3	0.00425727	121.514
+4000	3	0.3048	71.3	0.00425727	120.264
+5000	3	0.3048	71.3	0.00425727	118.134
+6300	3	0.3048	71.3	0.00425727	116.134
+8000	3	0.3048	71.3	0.00425727	114.634
+10000	3	0.3048	71.3	0.00425727	110.224
+400	3	0.3048	55.5	0.00452492	126.159
+500	3	0.3048	55.5	0.00452492	128.179
+630	3	0.3048	55.5	0.00452492	129.569
+800	3	0.3048	55.5	0.00452492	129.949
+1000	3	0.3048	55.5	0.00452492	129.329
+1250	3	0.3048	55.5	0.00452492	127.329
+1600	3	0.3048	55.5	0.00452492	124.439
+2000	3	0.3048	55.5	0.00452492	123.069
+2500	3	0.3048	55.5	0.00452492	122.439
+3150	3	0.3048	55.5	0.00452492	120.189
+4000	3	0.3048	55.5	0.00452492	118.689
+5000	3	0.3048	55.5	0.00452492	117.309
+6300	3	0.3048	55.5	0.00452492	115.679
+8000	3	0.3048	55.5	0.00452492	113.799
+10000	3	0.3048	55.5	0.00452492	112.169
+315	3	0.3048	39.6	0.00495741	123.312
+400	3	0.3048	39.6	0.00495741	125.472
+500	3	0.3048	39.6	0.00495741	127.632
+630	3	0.3048	39.6	0.00495741	129.292
+800	3	0.3048	39.6	0.00495741	129.552
+1000	3	0.3048	39.6	0.00495741	128.312
+1250	3	0.3048	39.6	0.00495741	125.802
+1600	3	0.3048	39.6	0.00495741	122.782
+2000	3	0.3048	39.6	0.00495741	120.532
+2500	3	0.3048	39.6	0.00495741	120.162
+3150	3	0.3048	39.6	0.00495741	118.922
+4000	3	0.3048	39.6	0.00495741	116.792
+5000	3	0.3048	39.6	0.00495741	115.792
+6300	3	0.3048	39.6	0.00495741	114.042
+8000	3	0.3048	39.6	0.00495741	110.652
+315	3	0.3048	31.7	0.00529514	123.118
+400	3	0.3048	31.7	0.00529514	125.398
+500	3	0.3048	31.7	0.00529514	127.548
+630	3	0.3048	31.7	0.00529514	128.698
+800	3	0.3048	31.7	0.00529514	128.708
+1000	3	0.3048	31.7	0.00529514	126.838
+1250	3	0.3048	31.7	0.00529514	124.838
+1600	3	0.3048	31.7	0.00529514	122.088
+2000	3	0.3048	31.7	0.00529514	120.088
+2500	3	0.3048	31.7	0.00529514	119.598
+3150	3	0.3048	31.7	0.00529514	118.108
+4000	3	0.3048	31.7	0.00529514	115.608
+5000	3	0.3048	31.7	0.00529514	113.858
+6300	3	0.3048	31.7	0.00529514	109.718
+250	4	0.3048	71.3	0.00497773	126.395
+315	4	0.3048	71.3	0.00497773	128.175
+400	4	0.3048	71.3	0.00497773	129.575
+500	4	0.3048	71.3	0.00497773	130.715
+630	4	0.3048	71.3	0.00497773	131.615
+800	4	0.3048	71.3	0.00497773	131.755
+1000	4	0.3048	71.3	0.00497773	131.015
+1250	4	0.3048	71.3	0.00497773	129.395
+1600	4	0.3048	71.3	0.00497773	126.645
+2000	4	0.3048	71.3	0.00497773	124.395
+2500	4	0.3048	71.3	0.00497773	123.775
+3150	4	0.3048	71.3	0.00497773	121.775
+4000	4	0.3048	71.3	0.00497773	119.535
+5000	4	0.3048	71.3	0.00497773	117.785
+6300	4	0.3048	71.3	0.00497773	116.165
+8000	4	0.3048	71.3	0.00497773	113.665
+10000	4	0.3048	71.3	0.00497773	110.905
+12500	4	0.3048	71.3	0.00497773	107.405
+250	4	0.3048	39.6	0.00579636	123.543
+315	4	0.3048	39.6	0.00579636	126.843
+400	4	0.3048	39.6	0.00579636	128.633
+500	4	0.3048	39.6	0.00579636	130.173
+630	4	0.3048	39.6	0.00579636	131.073
+800	4	0.3048	39.6	0.00579636	130.723
+1000	4	0.3048	39.6	0.00579636	128.723
+1250	4	0.3048	39.6	0.00579636	126.343
+1600	4	0.3048	39.6	0.00579636	123.213
+2000	4	0.3048	39.6	0.00579636	120.963
+2500	4	0.3048	39.6	0.00579636	120.233
+3150	4	0.3048	39.6	0.00579636	118.743
+4000	4	0.3048	39.6	0.00579636	115.863
+5000	4	0.3048	39.6	0.00579636	113.733
+1250	0	0.2286	71.3	0.00214345	128.144
+1600	0	0.2286	71.3	0.00214345	129.134
+2000	0	0.2286	71.3	0.00214345	128.244
+2500	0	0.2286	71.3	0.00214345	128.354
+3150	0	0.2286	71.3	0.00214345	127.834
+4000	0	0.2286	71.3	0.00214345	125.824
+5000	0	0.2286	71.3	0.00214345	124.304
+6300	0	0.2286	71.3	0.00214345	122.044
+8000	0	0.2286	71.3	0.00214345	118.024
+10000	0	0.2286	71.3	0.00214345	118.134
+12500	0	0.2286	71.3	0.00214345	117.624
+16000	0	0.2286	71.3	0.00214345	114.984
+20000	0	0.2286	71.3	0.00214345	114.474
+315	0	0.2286	55.5	0.00229336	119.540
+400	0	0.2286	55.5	0.00229336	121.660
+500	0	0.2286	55.5	0.00229336	123.780
+630	0	0.2286	55.5	0.00229336	126.160
+800	0	0.2286	55.5	0.00229336	127.530
+1000	0	0.2286	55.5	0.00229336	128.290
+1250	0	0.2286	55.5	0.00229336	127.910
+1600	0	0.2286	55.5	0.00229336	126.790
+2000	0	0.2286	55.5	0.00229336	126.540
+2500	0	0.2286	55.5	0.00229336	126.540
+3150	0	0.2286	55.5	0.00229336	125.160
+4000	0	0.2286	55.5	0.00229336	123.410
+5000	0	0.2286	55.5	0.00229336	122.410
+6300	0	0.2286	55.5	0.00229336	118.410
+315	0	0.2286	39.6	0.00253511	121.055
+400	0	0.2286	39.6	0.00253511	123.565
+500	0	0.2286	39.6	0.00253511	126.195
+630	0	0.2286	39.6	0.00253511	128.705
+800	0	0.2286	39.6	0.00253511	130.205
+1000	0	0.2286	39.6	0.00253511	130.435
+1250	0	0.2286	39.6	0.00253511	129.395
+1600	0	0.2286	39.6	0.00253511	127.095
+2000	0	0.2286	39.6	0.00253511	125.305
+2500	0	0.2286	39.6	0.00253511	125.025
+3150	0	0.2286	39.6	0.00253511	124.625
+4000	0	0.2286	39.6	0.00253511	123.465
+5000	0	0.2286	39.6	0.00253511	122.175
+6300	0	0.2286	39.6	0.00253511	117.465
+315	0	0.2286	31.7	0.0027238	120.595
+400	0	0.2286	31.7	0.0027238	123.635
+500	0	0.2286	31.7	0.0027238	126.675
+630	0	0.2286	31.7	0.0027238	129.465
+800	0	0.2286	31.7	0.0027238	130.725
+1000	0	0.2286	31.7	0.0027238	130.595
+1250	0	0.2286	31.7	0.0027238	128.805
+1600	0	0.2286	31.7	0.0027238	125.625
+2000	0	0.2286	31.7	0.0027238	123.455
+2500	0	0.2286	31.7	0.0027238	123.445
+3150	0	0.2286	31.7	0.0027238	123.445
+4000	0	0.2286	31.7	0.0027238	122.035
+5000	0	0.2286	31.7	0.0027238	120.505
+6300	0	0.2286	31.7	0.0027238	116.815
+400	2	0.2286	71.3	0.00293031	125.116
+500	2	0.2286	71.3	0.00293031	126.486
+630	2	0.2286	71.3	0.00293031	127.356
+800	2	0.2286	71.3	0.00293031	128.216
+1000	2	0.2286	71.3	0.00293031	128.956
+1250	2	0.2286	71.3	0.00293031	128.816
+1600	2	0.2286	71.3	0.00293031	127.796
+2000	2	0.2286	71.3	0.00293031	126.896
+2500	2	0.2286	71.3	0.00293031	127.006
+3150	2	0.2286	71.3	0.00293031	126.116
+4000	2	0.2286	71.3	0.00293031	124.086
+5000	2	0.2286	71.3	0.00293031	122.816
+6300	2	0.2286	71.3	0.00293031	120.786
+8000	2	0.2286	71.3	0.00293031	115.996
+10000	2	0.2286	71.3	0.00293031	113.086
+400	2	0.2286	55.5	0.00313525	122.292
+500	2	0.2286	55.5	0.00313525	124.692
+630	2	0.2286	55.5	0.00313525	126.842
+800	2	0.2286	55.5	0.00313525	128.492
+1000	2	0.2286	55.5	0.00313525	129.002
+1250	2	0.2286	55.5	0.00313525	128.762
+1600	2	0.2286	55.5	0.00313525	126.752
+2000	2	0.2286	55.5	0.00313525	124.612
+2500	2	0.2286	55.5	0.00313525	123.862
+3150	2	0.2286	55.5	0.00313525	123.742
+4000	2	0.2286	55.5	0.00313525	122.232
+5000	2	0.2286	55.5	0.00313525	120.472
+6300	2	0.2286	55.5	0.00313525	118.712
+315	2	0.2286	39.6	0.00346574	120.137
+400	2	0.2286	39.6	0.00346574	122.147
+500	2	0.2286	39.6	0.00346574	125.157
+630	2	0.2286	39.6	0.00346574	127.417
+800	2	0.2286	39.6	0.00346574	129.037
+1000	2	0.2286	39.6	0.00346574	129.147
+1250	2	0.2286	39.6	0.00346574	128.257
+1600	2	0.2286	39.6	0.00346574	125.837
+2000	2	0.2286	39.6	0.00346574	122.797
+2500	2	0.2286	39.6	0.00346574	121.397
+3150	2	0.2286	39.6	0.00346574	121.627
+4000	2	0.2286	39.6	0.00346574	120.227
+5000	2	0.2286	39.6	0.00346574	118.827
+6300	2	0.2286	39.6	0.00346574	116.417
+315	2	0.2286	31.7	0.00372371	120.147
+400	2	0.2286	31.7	0.00372371	123.417
+500	2	0.2286	31.7	0.00372371	126.677
+630	2	0.2286	31.7	0.00372371	129.057
+800	2	0.2286	31.7	0.00372371	130.307
+1000	2	0.2286	31.7	0.00372371	130.307
+1250	2	0.2286	31.7	0.00372371	128.677
+1600	2	0.2286	31.7	0.00372371	125.797
+2000	2	0.2286	31.7	0.00372371	123.037
+2500	2	0.2286	31.7	0.00372371	121.407
+3150	2	0.2286	31.7	0.00372371	121.527
+4000	2	0.2286	31.7	0.00372371	120.527
+5000	2	0.2286	31.7	0.00372371	118.267
+6300	2	0.2286	31.7	0.00372371	115.137
+500	4	0.2286	71.3	0.00400603	126.758
+630	4	0.2286	71.3	0.00400603	129.038
+800	4	0.2286	71.3	0.00400603	130.688
+1000	4	0.2286	71.3	0.00400603	131.708
+1250	4	0.2286	71.3	0.00400603	131.718
+1600	4	0.2286	71.3	0.00400603	129.468
+2000	4	0.2286	71.3	0.00400603	126.218
+2500	4	0.2286	71.3	0.00400603	124.338
+3150	4	0.2286	71.3	0.00400603	124.108
+4000	4	0.2286	71.3	0.00400603	121.728
+5000	4	0.2286	71.3	0.00400603	121.118
+6300	4	0.2286	71.3	0.00400603	118.618
+8000	4	0.2286	71.3	0.00400603	112.848
+10000	4	0.2286	71.3	0.00400603	113.108
+12500	4	0.2286	71.3	0.00400603	114.258
+16000	4	0.2286	71.3	0.00400603	112.768
+20000	4	0.2286	71.3	0.00400603	109.638
+400	4	0.2286	55.5	0.0042862	123.274
+500	4	0.2286	55.5	0.0042862	127.314
+630	4	0.2286	55.5	0.0042862	129.964
+800	4	0.2286	55.5	0.0042862	131.864
+1000	4	0.2286	55.5	0.0042862	132.134
+1250	4	0.2286	55.5	0.0042862	131.264
+1600	4	0.2286	55.5	0.0042862	128.264
+2000	4	0.2286	55.5	0.0042862	124.254
+2500	4	0.2286	55.5	0.0042862	122.384
+3150	4	0.2286	55.5	0.0042862	122.394
+4000	4	0.2286	55.5	0.0042862	120.654
+5000	4	0.2286	55.5	0.0042862	120.034
+6300	4	0.2286	55.5	0.0042862	117.154
+8000	4	0.2286	55.5	0.0042862	112.524
+315	4	0.2286	39.6	0.00473801	122.229
+400	4	0.2286	39.6	0.00473801	123.879
+500	4	0.2286	39.6	0.00473801	127.039
+630	4	0.2286	39.6	0.00473801	129.579
+800	4	0.2286	39.6	0.00473801	130.469
+1000	4	0.2286	39.6	0.00473801	129.969
+1250	4	0.2286	39.6	0.00473801	128.339
+1600	4	0.2286	39.6	0.00473801	125.319
+2000	4	0.2286	39.6	0.00473801	121.659
+2500	4	0.2286	39.6	0.00473801	119.649
+3150	4	0.2286	39.6	0.00473801	120.419
+4000	4	0.2286	39.6	0.00473801	119.159
+5000	4	0.2286	39.6	0.00473801	117.649
+6300	4	0.2286	39.6	0.00473801	114.249
+8000	4	0.2286	39.6	0.00473801	113.129
+250	4	0.2286	31.7	0.00509068	120.189
+315	4	0.2286	31.7	0.00509068	123.609
+400	4	0.2286	31.7	0.00509068	126.149
+500	4	0.2286	31.7	0.00509068	128.939
+630	4	0.2286	31.7	0.00509068	130.349
+800	4	0.2286	31.7	0.00509068	130.869
+1000	4	0.2286	31.7	0.00509068	129.869
+1250	4	0.2286	31.7	0.00509068	128.119
+1600	4	0.2286	31.7	0.00509068	125.229
+2000	4	0.2286	31.7	0.00509068	122.089
+2500	4	0.2286	31.7	0.00509068	120.209
+3150	4	0.2286	31.7	0.00509068	120.229
+4000	4	0.2286	31.7	0.00509068	118.859
+5000	4	0.2286	31.7	0.00509068	115.969
+6300	4	0.2286	31.7	0.00509068	112.699
+400	5.3	0.2286	71.3	0.0051942	127.700
+500	5.3	0.2286	71.3	0.0051942	129.880
+630	5.3	0.2286	71.3	0.0051942	131.800
+800	5.3	0.2286	71.3	0.0051942	133.480
+1000	5.3	0.2286	71.3	0.0051942	134.000
+1250	5.3	0.2286	71.3	0.0051942	133.380
+1600	5.3	0.2286	71.3	0.0051942	130.460
+2000	5.3	0.2286	71.3	0.0051942	125.890
+2500	5.3	0.2286	71.3	0.0051942	123.740
+3150	5.3	0.2286	71.3	0.0051942	123.120
+4000	5.3	0.2286	71.3	0.0051942	120.330
+5000	5.3	0.2286	71.3	0.0051942	118.050
+6300	5.3	0.2286	71.3	0.0051942	116.920
+8000	5.3	0.2286	71.3	0.0051942	114.900
+10000	5.3	0.2286	71.3	0.0051942	111.350
+250	5.3	0.2286	39.6	0.00614329	127.011
+315	5.3	0.2286	39.6	0.00614329	129.691
+400	5.3	0.2286	39.6	0.00614329	131.221
+500	5.3	0.2286	39.6	0.00614329	132.251
+630	5.3	0.2286	39.6	0.00614329	132.011
+800	5.3	0.2286	39.6	0.00614329	129.491
+1000	5.3	0.2286	39.6	0.00614329	125.581
+1250	5.3	0.2286	39.6	0.00614329	125.721
+1600	5.3	0.2286	39.6	0.00614329	123.081
+2000	5.3	0.2286	39.6	0.00614329	117.911
+2500	5.3	0.2286	39.6	0.00614329	116.151
+3150	5.3	0.2286	39.6	0.00614329	118.441
+4000	5.3	0.2286	39.6	0.00614329	115.801
+5000	5.3	0.2286	39.6	0.00614329	115.311
+6300	5.3	0.2286	39.6	0.00614329	112.541
+200	7.3	0.2286	71.3	0.0104404	138.758
+250	7.3	0.2286	71.3	0.0104404	139.918
+315	7.3	0.2286	71.3	0.0104404	139.808
+400	7.3	0.2286	71.3	0.0104404	139.438
+500	7.3	0.2286	71.3	0.0104404	136.798
+630	7.3	0.2286	71.3	0.0104404	133.768
+800	7.3	0.2286	71.3	0.0104404	130.748
+1000	7.3	0.2286	71.3	0.0104404	126.838
+1250	7.3	0.2286	71.3	0.0104404	127.358
+1600	7.3	0.2286	71.3	0.0104404	125.728
+2000	7.3	0.2286	71.3	0.0104404	122.708
+2500	7.3	0.2286	71.3	0.0104404	122.088
+3150	7.3	0.2286	71.3	0.0104404	120.458
+4000	7.3	0.2286	71.3	0.0104404	119.208
+5000	7.3	0.2286	71.3	0.0104404	115.298
+6300	7.3	0.2286	71.3	0.0104404	115.818
+200	7.3	0.2286	55.5	0.0111706	135.234
+250	7.3	0.2286	55.5	0.0111706	136.384
+315	7.3	0.2286	55.5	0.0111706	136.284
+400	7.3	0.2286	55.5	0.0111706	135.924
+500	7.3	0.2286	55.5	0.0111706	133.174
+630	7.3	0.2286	55.5	0.0111706	130.934
+800	7.3	0.2286	55.5	0.0111706	128.444
+1000	7.3	0.2286	55.5	0.0111706	125.194
+1250	7.3	0.2286	55.5	0.0111706	125.724
+1600	7.3	0.2286	55.5	0.0111706	123.354
+2000	7.3	0.2286	55.5	0.0111706	120.354
+2500	7.3	0.2286	55.5	0.0111706	118.994
+3150	7.3	0.2286	55.5	0.0111706	117.134
+4000	7.3	0.2286	55.5	0.0111706	117.284
+5000	7.3	0.2286	55.5	0.0111706	113.144
+6300	7.3	0.2286	55.5	0.0111706	111.534
+200	7.3	0.2286	39.6	0.0123481	130.989
+250	7.3	0.2286	39.6	0.0123481	131.889
+315	7.3	0.2286	39.6	0.0123481	132.149
+400	7.3	0.2286	39.6	0.0123481	132.039
+500	7.3	0.2286	39.6	0.0123481	130.299
+630	7.3	0.2286	39.6	0.0123481	128.929
+800	7.3	0.2286	39.6	0.0123481	126.299
+1000	7.3	0.2286	39.6	0.0123481	122.539
+1250	7.3	0.2286	39.6	0.0123481	123.189
+1600	7.3	0.2286	39.6	0.0123481	121.059
+2000	7.3	0.2286	39.6	0.0123481	117.809
+2500	7.3	0.2286	39.6	0.0123481	116.559
+3150	7.3	0.2286	39.6	0.0123481	114.309
+4000	7.3	0.2286	39.6	0.0123481	114.079
+5000	7.3	0.2286	39.6	0.0123481	111.959
+6300	7.3	0.2286	39.6	0.0123481	110.839
+200	7.3	0.2286	31.7	0.0132672	128.679
+250	7.3	0.2286	31.7	0.0132672	130.089
+315	7.3	0.2286	31.7	0.0132672	130.239
+400	7.3	0.2286	31.7	0.0132672	130.269
+500	7.3	0.2286	31.7	0.0132672	128.169
+630	7.3	0.2286	31.7	0.0132672	126.189
+800	7.3	0.2286	31.7	0.0132672	123.209
+1000	7.3	0.2286	31.7	0.0132672	119.099
+1250	7.3	0.2286	31.7	0.0132672	120.509
+1600	7.3	0.2286	31.7	0.0132672	119.039
+2000	7.3	0.2286	31.7	0.0132672	115.309
+2500	7.3	0.2286	31.7	0.0132672	114.709
+3150	7.3	0.2286	31.7	0.0132672	113.229
+4000	7.3	0.2286	31.7	0.0132672	112.639
+5000	7.3	0.2286	31.7	0.0132672	111.029
+6300	7.3	0.2286	31.7	0.0132672	110.689
+800	0	0.1524	71.3	0.0015988	125.817
+1000	0	0.1524	71.3	0.0015988	127.307
+1250	0	0.1524	71.3	0.0015988	128.927
+1600	0	0.1524	71.3	0.0015988	129.667
+2000	0	0.1524	71.3	0.0015988	128.647
+2500	0	0.1524	71.3	0.0015988	128.127
+3150	0	0.1524	71.3	0.0015988	129.377
+4000	0	0.1524	71.3	0.0015988	128.857
+5000	0	0.1524	71.3	0.0015988	126.457
+6300	0	0.1524	71.3	0.0015988	125.427
+8000	0	0.1524	71.3	0.0015988	122.527
+10000	0	0.1524	71.3	0.0015988	120.247
+12500	0	0.1524	71.3	0.0015988	117.087
+16000	0	0.1524	71.3	0.0015988	113.297
+500	0	0.1524	55.5	0.00172668	120.573
+630	0	0.1524	55.5	0.00172668	123.583
+800	0	0.1524	55.5	0.00172668	126.713
+1000	0	0.1524	55.5	0.00172668	128.583
+1250	0	0.1524	55.5	0.00172668	129.953
+1600	0	0.1524	55.5	0.00172668	130.183
+2000	0	0.1524	55.5	0.00172668	129.673
+2500	0	0.1524	55.5	0.00172668	127.763
+3150	0	0.1524	55.5	0.00172668	127.753
+4000	0	0.1524	55.5	0.00172668	127.233
+5000	0	0.1524	55.5	0.00172668	125.203
+6300	0	0.1524	55.5	0.00172668	123.303
+8000	0	0.1524	55.5	0.00172668	121.903
+10000	0	0.1524	55.5	0.00172668	119.253
+12500	0	0.1524	55.5	0.00172668	117.093
+16000	0	0.1524	55.5	0.00172668	112.803
+500	0	0.1524	39.6	0.00193287	119.513
+630	0	0.1524	39.6	0.00193287	124.403
+800	0	0.1524	39.6	0.00193287	127.903
+1000	0	0.1524	39.6	0.00193287	130.033
+1250	0	0.1524	39.6	0.00193287	131.023
+1600	0	0.1524	39.6	0.00193287	131.013
+2000	0	0.1524	39.6	0.00193287	129.633
+2500	0	0.1524	39.6	0.00193287	126.863
+3150	0	0.1524	39.6	0.00193287	125.603
+4000	0	0.1524	39.6	0.00193287	125.343
+5000	0	0.1524	39.6	0.00193287	123.453
+6300	0	0.1524	39.6	0.00193287	121.313
+8000	0	0.1524	39.6	0.00193287	120.553
+10000	0	0.1524	39.6	0.00193287	115.413
+500	0	0.1524	31.7	0.00209405	121.617
+630	0	0.1524	31.7	0.00209405	125.997
+800	0	0.1524	31.7	0.00209405	129.117
+1000	0	0.1524	31.7	0.00209405	130.987
+1250	0	0.1524	31.7	0.00209405	131.467
+1600	0	0.1524	31.7	0.00209405	130.817
+2000	0	0.1524	31.7	0.00209405	128.907
+2500	0	0.1524	31.7	0.00209405	125.867
+3150	0	0.1524	31.7	0.00209405	124.207
+4000	0	0.1524	31.7	0.00209405	123.807
+5000	0	0.1524	31.7	0.00209405	122.397
+6300	0	0.1524	31.7	0.00209405	119.737
+8000	0	0.1524	31.7	0.00209405	117.957
+630	2.7	0.1524	71.3	0.00243851	127.404
+800	2.7	0.1524	71.3	0.00243851	127.394
+1000	2.7	0.1524	71.3	0.00243851	128.774
+1250	2.7	0.1524	71.3	0.00243851	130.144
+1600	2.7	0.1524	71.3	0.00243851	130.644
+2000	2.7	0.1524	71.3	0.00243851	130.114
+2500	2.7	0.1524	71.3	0.00243851	128.334
+3150	2.7	0.1524	71.3	0.00243851	127.054
+4000	2.7	0.1524	71.3	0.00243851	126.534
+5000	2.7	0.1524	71.3	0.00243851	124.364
+6300	2.7	0.1524	71.3	0.00243851	121.944
+8000	2.7	0.1524	71.3	0.00243851	120.534
+10000	2.7	0.1524	71.3	0.00243851	116.724
+12500	2.7	0.1524	71.3	0.00243851	113.034
+16000	2.7	0.1524	71.3	0.00243851	110.364
+500	2.7	0.1524	39.6	0.00294804	121.009
+630	2.7	0.1524	39.6	0.00294804	125.809
+800	2.7	0.1524	39.6	0.00294804	128.829
+1000	2.7	0.1524	39.6	0.00294804	130.589
+1250	2.7	0.1524	39.6	0.00294804	130.829
+1600	2.7	0.1524	39.6	0.00294804	130.049
+2000	2.7	0.1524	39.6	0.00294804	128.139
+2500	2.7	0.1524	39.6	0.00294804	125.589
+3150	2.7	0.1524	39.6	0.00294804	122.919
+4000	2.7	0.1524	39.6	0.00294804	121.889
+5000	2.7	0.1524	39.6	0.00294804	121.499
+6300	2.7	0.1524	39.6	0.00294804	119.209
+8000	2.7	0.1524	39.6	0.00294804	116.659
+10000	2.7	0.1524	39.6	0.00294804	112.589
+12500	2.7	0.1524	39.6	0.00294804	108.649
+400	5.4	0.1524	71.3	0.00401199	124.121
+500	5.4	0.1524	71.3	0.00401199	126.291
+630	5.4	0.1524	71.3	0.00401199	128.971
+800	5.4	0.1524	71.3	0.00401199	131.281
+1000	5.4	0.1524	71.3	0.00401199	133.201
+1250	5.4	0.1524	71.3	0.00401199	134.111
+1600	5.4	0.1524	71.3	0.00401199	133.241
+2000	5.4	0.1524	71.3	0.00401199	131.111
+2500	5.4	0.1524	71.3	0.00401199	127.591
+3150	5.4	0.1524	71.3	0.00401199	123.311
+4000	5.4	0.1524	71.3	0.00401199	121.431
+5000	5.4	0.1524	71.3	0.00401199	120.061
+6300	5.4	0.1524	71.3	0.00401199	116.411
+400	5.4	0.1524	55.5	0.00433288	126.807
+500	5.4	0.1524	55.5	0.00433288	129.367
+630	5.4	0.1524	55.5	0.00433288	131.807
+800	5.4	0.1524	55.5	0.00433288	133.097
+1000	5.4	0.1524	55.5	0.00433288	132.127
+1250	5.4	0.1524	55.5	0.00433288	130.777
+1600	5.4	0.1524	55.5	0.00433288	130.567
+2000	5.4	0.1524	55.5	0.00433288	128.707
+2500	5.4	0.1524	55.5	0.00433288	124.077
+3150	5.4	0.1524	55.5	0.00433288	121.587
+4000	5.4	0.1524	55.5	0.00433288	119.737
+5000	5.4	0.1524	55.5	0.00433288	118.757
+6300	5.4	0.1524	55.5	0.00433288	117.287
+8000	5.4	0.1524	55.5	0.00433288	114.927
+315	5.4	0.1524	39.6	0.00485029	125.347
+400	5.4	0.1524	39.6	0.00485029	127.637
+500	5.4	0.1524	39.6	0.00485029	129.937
+630	5.4	0.1524	39.6	0.00485029	132.357
+800	5.4	0.1524	39.6	0.00485029	132.757
+1000	5.4	0.1524	39.6	0.00485029	130.507
+1250	5.4	0.1524	39.6	0.00485029	127.117
+1600	5.4	0.1524	39.6	0.00485029	126.267
+2000	5.4	0.1524	39.6	0.00485029	124.647
+2500	5.4	0.1524	39.6	0.00485029	120.497
+3150	5.4	0.1524	39.6	0.00485029	119.137
+4000	5.4	0.1524	39.6	0.00485029	117.137
+5000	5.4	0.1524	39.6	0.00485029	117.037
+6300	5.4	0.1524	39.6	0.00485029	116.677
+315	5.4	0.1524	31.7	0.00525474	125.741
+400	5.4	0.1524	31.7	0.00525474	127.781
+500	5.4	0.1524	31.7	0.00525474	129.681
+630	5.4	0.1524	31.7	0.00525474	131.471
+800	5.4	0.1524	31.7	0.00525474	131.491
+1000	5.4	0.1524	31.7	0.00525474	128.241
+1250	5.4	0.1524	31.7	0.00525474	123.991
+1600	5.4	0.1524	31.7	0.00525474	123.761
+2000	5.4	0.1524	31.7	0.00525474	122.771
+2500	5.4	0.1524	31.7	0.00525474	119.151
+3150	5.4	0.1524	31.7	0.00525474	118.291
+4000	5.4	0.1524	31.7	0.00525474	116.181
+5000	5.4	0.1524	31.7	0.00525474	115.691
+6300	5.4	0.1524	31.7	0.00525474	115.591
+315	7.2	0.1524	71.3	0.00752039	128.713
+400	7.2	0.1524	71.3	0.00752039	130.123
+500	7.2	0.1524	71.3	0.00752039	132.043
+630	7.2	0.1524	71.3	0.00752039	134.853
+800	7.2	0.1524	71.3	0.00752039	136.023
+1000	7.2	0.1524	71.3	0.00752039	134.273
+1250	7.2	0.1524	71.3	0.00752039	132.513
+1600	7.2	0.1524	71.3	0.00752039	130.893
+2000	7.2	0.1524	71.3	0.00752039	128.643
+2500	7.2	0.1524	71.3	0.00752039	124.353
+3150	7.2	0.1524	71.3	0.00752039	116.783
+4000	7.2	0.1524	71.3	0.00752039	119.343
+5000	7.2	0.1524	71.3	0.00752039	118.343
+6300	7.2	0.1524	71.3	0.00752039	116.603
+8000	7.2	0.1524	71.3	0.00752039	113.333
+10000	7.2	0.1524	71.3	0.00752039	110.313
+250	7.2	0.1524	39.6	0.00909175	127.488
+315	7.2	0.1524	39.6	0.00909175	130.558
+400	7.2	0.1524	39.6	0.00909175	132.118
+500	7.2	0.1524	39.6	0.00909175	132.658
+630	7.2	0.1524	39.6	0.00909175	133.198
+800	7.2	0.1524	39.6	0.00909175	132.358
+1000	7.2	0.1524	39.6	0.00909175	128.338
+1250	7.2	0.1524	39.6	0.00909175	122.428
+1600	7.2	0.1524	39.6	0.00909175	120.058
+2000	7.2	0.1524	39.6	0.00909175	120.228
+2500	7.2	0.1524	39.6	0.00909175	117.478
+3150	7.2	0.1524	39.6	0.00909175	111.818
+4000	7.2	0.1524	39.6	0.00909175	114.258
+5000	7.2	0.1524	39.6	0.00909175	113.288
+6300	7.2	0.1524	39.6	0.00909175	112.688
+8000	7.2	0.1524	39.6	0.00909175	111.588
+10000	7.2	0.1524	39.6	0.00909175	110.868
+200	9.9	0.1524	71.3	0.0193001	134.319
+250	9.9	0.1524	71.3	0.0193001	135.329
+315	9.9	0.1524	71.3	0.0193001	135.459
+400	9.9	0.1524	71.3	0.0193001	135.079
+500	9.9	0.1524	71.3	0.0193001	131.279
+630	9.9	0.1524	71.3	0.0193001	129.889
+800	9.9	0.1524	71.3	0.0193001	128.879
+1000	9.9	0.1524	71.3	0.0193001	126.349
+1250	9.9	0.1524	71.3	0.0193001	122.679
+1600	9.9	0.1524	71.3	0.0193001	121.789
+2000	9.9	0.1524	71.3	0.0193001	120.779
+2500	9.9	0.1524	71.3	0.0193001	119.639
+3150	9.9	0.1524	71.3	0.0193001	116.849
+4000	9.9	0.1524	71.3	0.0193001	115.079
+5000	9.9	0.1524	71.3	0.0193001	114.569
+6300	9.9	0.1524	71.3	0.0193001	112.039
+200	9.9	0.1524	55.5	0.0208438	131.955
+250	9.9	0.1524	55.5	0.0208438	133.235
+315	9.9	0.1524	55.5	0.0208438	132.355
+400	9.9	0.1524	55.5	0.0208438	131.605
+500	9.9	0.1524	55.5	0.0208438	127.815
+630	9.9	0.1524	55.5	0.0208438	127.315
+800	9.9	0.1524	55.5	0.0208438	126.565
+1000	9.9	0.1524	55.5	0.0208438	124.665
+1250	9.9	0.1524	55.5	0.0208438	121.635
+1600	9.9	0.1524	55.5	0.0208438	119.875
+2000	9.9	0.1524	55.5	0.0208438	119.505
+2500	9.9	0.1524	55.5	0.0208438	118.365
+3150	9.9	0.1524	55.5	0.0208438	115.085
+4000	9.9	0.1524	55.5	0.0208438	112.945
+5000	9.9	0.1524	55.5	0.0208438	112.065
+6300	9.9	0.1524	55.5	0.0208438	110.555
+200	9.9	0.1524	39.6	0.0233328	127.315
+250	9.9	0.1524	39.6	0.0233328	128.335
+315	9.9	0.1524	39.6	0.0233328	128.595
+400	9.9	0.1524	39.6	0.0233328	128.345
+500	9.9	0.1524	39.6	0.0233328	126.835
+630	9.9	0.1524	39.6	0.0233328	126.465
+800	9.9	0.1524	39.6	0.0233328	126.345
+1000	9.9	0.1524	39.6	0.0233328	123.835
+1250	9.9	0.1524	39.6	0.0233328	120.555
+1600	9.9	0.1524	39.6	0.0233328	118.545
+2000	9.9	0.1524	39.6	0.0233328	117.925
+2500	9.9	0.1524	39.6	0.0233328	116.295
+3150	9.9	0.1524	39.6	0.0233328	113.525
+4000	9.9	0.1524	39.6	0.0233328	112.265
+5000	9.9	0.1524	39.6	0.0233328	111.135
+6300	9.9	0.1524	39.6	0.0233328	109.885
+200	9.9	0.1524	31.7	0.0252785	127.299
+250	9.9	0.1524	31.7	0.0252785	128.559
+315	9.9	0.1524	31.7	0.0252785	128.809
+400	9.9	0.1524	31.7	0.0252785	128.939
+500	9.9	0.1524	31.7	0.0252785	127.179
+630	9.9	0.1524	31.7	0.0252785	126.049
+800	9.9	0.1524	31.7	0.0252785	125.539
+1000	9.9	0.1524	31.7	0.0252785	122.149
+1250	9.9	0.1524	31.7	0.0252785	118.619
+1600	9.9	0.1524	31.7	0.0252785	117.119
+2000	9.9	0.1524	31.7	0.0252785	116.859
+2500	9.9	0.1524	31.7	0.0252785	114.729
+3150	9.9	0.1524	31.7	0.0252785	112.209
+4000	9.9	0.1524	31.7	0.0252785	111.459
+5000	9.9	0.1524	31.7	0.0252785	109.949
+6300	9.9	0.1524	31.7	0.0252785	108.689
+200	12.6	0.1524	71.3	0.0483159	128.354
+250	12.6	0.1524	71.3	0.0483159	129.744
+315	12.6	0.1524	71.3	0.0483159	128.484
+400	12.6	0.1524	71.3	0.0483159	127.094
+500	12.6	0.1524	71.3	0.0483159	121.664
+630	12.6	0.1524	71.3	0.0483159	123.304
+800	12.6	0.1524	71.3	0.0483159	123.054
+1000	12.6	0.1524	71.3	0.0483159	122.044
+1250	12.6	0.1524	71.3	0.0483159	120.154
+1600	12.6	0.1524	71.3	0.0483159	120.534
+2000	12.6	0.1524	71.3	0.0483159	117.504
+2500	12.6	0.1524	71.3	0.0483159	115.234
+3150	12.6	0.1524	71.3	0.0483159	113.334
+4000	12.6	0.1524	71.3	0.0483159	108.034
+5000	12.6	0.1524	71.3	0.0483159	108.034
+6300	12.6	0.1524	71.3	0.0483159	107.284
+200	12.6	0.1524	39.6	0.0584113	114.750
+250	12.6	0.1524	39.6	0.0584113	115.890
+315	12.6	0.1524	39.6	0.0584113	116.020
+400	12.6	0.1524	39.6	0.0584113	115.910
+500	12.6	0.1524	39.6	0.0584113	114.900
+630	12.6	0.1524	39.6	0.0584113	116.550
+800	12.6	0.1524	39.6	0.0584113	116.560
+1000	12.6	0.1524	39.6	0.0584113	114.670
+1250	12.6	0.1524	39.6	0.0584113	112.160
+1600	12.6	0.1524	39.6	0.0584113	110.780
+2000	12.6	0.1524	39.6	0.0584113	109.520
+2500	12.6	0.1524	39.6	0.0584113	106.880
+3150	12.6	0.1524	39.6	0.0584113	106.260
+4000	12.6	0.1524	39.6	0.0584113	104.500
+5000	12.6	0.1524	39.6	0.0584113	104.130
+6300	12.6	0.1524	39.6	0.0584113	103.380
+800	0	0.0508	71.3	0.000740478	130.960
+1000	0	0.0508	71.3	0.000740478	129.450
+1250	0	0.0508	71.3	0.000740478	128.560
+1600	0	0.0508	71.3	0.000740478	129.680
+2000	0	0.0508	71.3	0.000740478	131.060
+2500	0	0.0508	71.3	0.000740478	131.310
+3150	0	0.0508	71.3	0.000740478	135.070
+4000	0	0.0508	71.3	0.000740478	134.430
+5000	0	0.0508	71.3	0.000740478	134.430
+6300	0	0.0508	71.3	0.000740478	133.040
+8000	0	0.0508	71.3	0.000740478	130.890
+10000	0	0.0508	71.3	0.000740478	128.740
+12500	0	0.0508	71.3	0.000740478	125.220
+800	0	0.0508	55.5	0.00076193	124.336
+1000	0	0.0508	55.5	0.00076193	125.586
+1250	0	0.0508	55.5	0.00076193	127.076
+1600	0	0.0508	55.5	0.00076193	128.576
+2000	0	0.0508	55.5	0.00076193	131.456
+2500	0	0.0508	55.5	0.00076193	133.956
+3150	0	0.0508	55.5	0.00076193	134.826
+4000	0	0.0508	55.5	0.00076193	134.946
+5000	0	0.0508	55.5	0.00076193	134.556
+6300	0	0.0508	55.5	0.00076193	132.796
+8000	0	0.0508	55.5	0.00076193	130.156
+10000	0	0.0508	55.5	0.00076193	127.636
+12500	0	0.0508	55.5	0.00076193	125.376
+800	0	0.0508	39.6	0.000791822	126.508
+1000	0	0.0508	39.6	0.000791822	127.638
+1250	0	0.0508	39.6	0.000791822	129.148
+1600	0	0.0508	39.6	0.000791822	130.908
+2000	0	0.0508	39.6	0.000791822	132.918
+2500	0	0.0508	39.6	0.000791822	134.938
+3150	0	0.0508	39.6	0.000791822	135.938
+4000	0	0.0508	39.6	0.000791822	135.308
+5000	0	0.0508	39.6	0.000791822	134.308
+6300	0	0.0508	39.6	0.000791822	131.918
+8000	0	0.0508	39.6	0.000791822	128.518
+10000	0	0.0508	39.6	0.000791822	125.998
+12500	0	0.0508	39.6	0.000791822	123.988
+800	0	0.0508	31.7	0.000812164	122.790
+1000	0	0.0508	31.7	0.000812164	126.780
+1250	0	0.0508	31.7	0.000812164	129.270
+1600	0	0.0508	31.7	0.000812164	131.010
+2000	0	0.0508	31.7	0.000812164	133.010
+2500	0	0.0508	31.7	0.000812164	134.870
+3150	0	0.0508	31.7	0.000812164	135.490
+4000	0	0.0508	31.7	0.000812164	134.110
+5000	0	0.0508	31.7	0.000812164	133.230
+6300	0	0.0508	31.7	0.000812164	130.340
+8000	0	0.0508	31.7	0.000812164	126.590
+10000	0	0.0508	31.7	0.000812164	122.450
+12500	0	0.0508	31.7	0.000812164	119.070
+1600	4.2	0.0508	71.3	0.00142788	124.318
+2000	4.2	0.0508	71.3	0.00142788	129.848
+2500	4.2	0.0508	71.3	0.00142788	131.978
+3150	4.2	0.0508	71.3	0.00142788	133.728
+4000	4.2	0.0508	71.3	0.00142788	133.598
+5000	4.2	0.0508	71.3	0.00142788	132.828
+6300	4.2	0.0508	71.3	0.00142788	129.308
+8000	4.2	0.0508	71.3	0.00142788	125.268
+10000	4.2	0.0508	71.3	0.00142788	121.238
+12500	4.2	0.0508	71.3	0.00142788	117.328
+1000	4.2	0.0508	39.6	0.00152689	125.647
+1250	4.2	0.0508	39.6	0.00152689	128.427
+1600	4.2	0.0508	39.6	0.00152689	130.197
+2000	4.2	0.0508	39.6	0.00152689	132.587
+2500	4.2	0.0508	39.6	0.00152689	133.847
+3150	4.2	0.0508	39.6	0.00152689	133.587
+4000	4.2	0.0508	39.6	0.00152689	131.807
+5000	4.2	0.0508	39.6	0.00152689	129.777
+6300	4.2	0.0508	39.6	0.00152689	125.717
+8000	4.2	0.0508	39.6	0.00152689	120.397
+10000	4.2	0.0508	39.6	0.00152689	116.967
+800	8.4	0.0508	71.3	0.00529514	127.556
+1000	8.4	0.0508	71.3	0.00529514	129.946
+1250	8.4	0.0508	71.3	0.00529514	132.086
+1600	8.4	0.0508	71.3	0.00529514	133.846
+2000	8.4	0.0508	71.3	0.00529514	134.476
+2500	8.4	0.0508	71.3	0.00529514	134.226
+3150	8.4	0.0508	71.3	0.00529514	131.966
+4000	8.4	0.0508	71.3	0.00529514	126.926
+5000	8.4	0.0508	71.3	0.00529514	121.146
+400	8.4	0.0508	55.5	0.00544854	121.582
+500	8.4	0.0508	55.5	0.00544854	123.742
+630	8.4	0.0508	55.5	0.00544854	126.152
+800	8.4	0.0508	55.5	0.00544854	128.562
+1000	8.4	0.0508	55.5	0.00544854	130.722
+1250	8.4	0.0508	55.5	0.00544854	132.252
+1600	8.4	0.0508	55.5	0.00544854	133.032
+2000	8.4	0.0508	55.5	0.00544854	133.042
+2500	8.4	0.0508	55.5	0.00544854	131.542
+3150	8.4	0.0508	55.5	0.00544854	128.402
+4000	8.4	0.0508	55.5	0.00544854	122.612
+5000	8.4	0.0508	55.5	0.00544854	115.812
+400	8.4	0.0508	39.6	0.00566229	120.015
+500	8.4	0.0508	39.6	0.00566229	122.905
+630	8.4	0.0508	39.6	0.00566229	126.045
+800	8.4	0.0508	39.6	0.00566229	128.435
+1000	8.4	0.0508	39.6	0.00566229	130.195
+1250	8.4	0.0508	39.6	0.00566229	131.205
+1600	8.4	0.0508	39.6	0.00566229	130.965
+2000	8.4	0.0508	39.6	0.00566229	129.965
+2500	8.4	0.0508	39.6	0.00566229	127.465
+3150	8.4	0.0508	39.6	0.00566229	123.965
+4000	8.4	0.0508	39.6	0.00566229	118.955
+400	8.4	0.0508	31.7	0.00580776	120.076
+500	8.4	0.0508	31.7	0.00580776	122.966
+630	8.4	0.0508	31.7	0.00580776	125.856
+800	8.4	0.0508	31.7	0.00580776	128.246
+1000	8.4	0.0508	31.7	0.00580776	129.516
+1250	8.4	0.0508	31.7	0.00580776	130.156
+1600	8.4	0.0508	31.7	0.00580776	129.296
+2000	8.4	0.0508	31.7	0.00580776	127.686
+2500	8.4	0.0508	31.7	0.00580776	125.576
+3150	8.4	0.0508	31.7	0.00580776	122.086
+4000	8.4	0.0508	31.7	0.00580776	118.106
+200	11.2	0.0508	71.3	0.014072	125.941
+250	11.2	0.0508	71.3	0.014072	127.101
+315	11.2	0.0508	71.3	0.014072	128.381
+400	11.2	0.0508	71.3	0.014072	129.281
+500	11.2	0.0508	71.3	0.014072	130.311
+630	11.2	0.0508	71.3	0.014072	133.611
+800	11.2	0.0508	71.3	0.014072	136.031
+1000	11.2	0.0508	71.3	0.014072	136.941
+1250	11.2	0.0508	71.3	0.014072	136.191
+1600	11.2	0.0508	71.3	0.014072	135.191
+2000	11.2	0.0508	71.3	0.014072	133.311
+2500	11.2	0.0508	71.3	0.014072	130.541
+3150	11.2	0.0508	71.3	0.014072	127.141
+4000	11.2	0.0508	71.3	0.014072	122.471
+200	11.2	0.0508	39.6	0.0150478	125.010
+250	11.2	0.0508	39.6	0.0150478	126.430
+315	11.2	0.0508	39.6	0.0150478	128.990
+400	11.2	0.0508	39.6	0.0150478	130.670
+500	11.2	0.0508	39.6	0.0150478	131.960
+630	11.2	0.0508	39.6	0.0150478	133.130
+800	11.2	0.0508	39.6	0.0150478	133.790
+1000	11.2	0.0508	39.6	0.0150478	132.430
+1250	11.2	0.0508	39.6	0.0150478	130.050
+1600	11.2	0.0508	39.6	0.0150478	126.540
+2000	11.2	0.0508	39.6	0.0150478	124.420
+2500	11.2	0.0508	39.6	0.0150478	122.170
+3150	11.2	0.0508	39.6	0.0150478	119.670
+4000	11.2	0.0508	39.6	0.0150478	115.520
+200	15.4	0.0508	71.3	0.0264269	123.595
+250	15.4	0.0508	71.3	0.0264269	124.835
+315	15.4	0.0508	71.3	0.0264269	126.195
+400	15.4	0.0508	71.3	0.0264269	126.805
+500	15.4	0.0508	71.3	0.0264269	127.285
+630	15.4	0.0508	71.3	0.0264269	129.645
+800	15.4	0.0508	71.3	0.0264269	131.515
+1000	15.4	0.0508	71.3	0.0264269	131.865
+1250	15.4	0.0508	71.3	0.0264269	130.845
+1600	15.4	0.0508	71.3	0.0264269	130.065
+2000	15.4	0.0508	71.3	0.0264269	129.285
+2500	15.4	0.0508	71.3	0.0264269	127.625
+3150	15.4	0.0508	71.3	0.0264269	125.715
+4000	15.4	0.0508	71.3	0.0264269	122.675
+5000	15.4	0.0508	71.3	0.0264269	119.135
+6300	15.4	0.0508	71.3	0.0264269	115.215
+8000	15.4	0.0508	71.3	0.0264269	112.675
+200	15.4	0.0508	55.5	0.0271925	122.940
+250	15.4	0.0508	55.5	0.0271925	124.170
+315	15.4	0.0508	55.5	0.0271925	125.390
+400	15.4	0.0508	55.5	0.0271925	126.500
+500	15.4	0.0508	55.5	0.0271925	127.220
+630	15.4	0.0508	55.5	0.0271925	129.330
+800	15.4	0.0508	55.5	0.0271925	130.430
+1000	15.4	0.0508	55.5	0.0271925	130.400
+1250	15.4	0.0508	55.5	0.0271925	130.000
+1600	15.4	0.0508	55.5	0.0271925	128.200
+2000	15.4	0.0508	55.5	0.0271925	127.040
+2500	15.4	0.0508	55.5	0.0271925	125.630
+3150	15.4	0.0508	55.5	0.0271925	123.460
+4000	15.4	0.0508	55.5	0.0271925	120.920
+5000	15.4	0.0508	55.5	0.0271925	117.110
+6300	15.4	0.0508	55.5	0.0271925	112.930
+200	15.4	0.0508	39.6	0.0282593	121.783
+250	15.4	0.0508	39.6	0.0282593	122.893
+315	15.4	0.0508	39.6	0.0282593	124.493
+400	15.4	0.0508	39.6	0.0282593	125.353
+500	15.4	0.0508	39.6	0.0282593	125.963
+630	15.4	0.0508	39.6	0.0282593	127.443
+800	15.4	0.0508	39.6	0.0282593	128.423
+1000	15.4	0.0508	39.6	0.0282593	127.893
+1250	15.4	0.0508	39.6	0.0282593	126.743
+1600	15.4	0.0508	39.6	0.0282593	124.843
+2000	15.4	0.0508	39.6	0.0282593	123.443
+2500	15.4	0.0508	39.6	0.0282593	122.413
+3150	15.4	0.0508	39.6	0.0282593	120.513
+4000	15.4	0.0508	39.6	0.0282593	118.113
+5000	15.4	0.0508	39.6	0.0282593	114.453
+6300	15.4	0.0508	39.6	0.0282593	109.663
+200	15.4	0.0508	31.7	0.0289853	119.975
+250	15.4	0.0508	31.7	0.0289853	121.225
+315	15.4	0.0508	31.7	0.0289853	122.845
+400	15.4	0.0508	31.7	0.0289853	123.705
+500	15.4	0.0508	31.7	0.0289853	123.695
+630	15.4	0.0508	31.7	0.0289853	124.685
+800	15.4	0.0508	31.7	0.0289853	125.555
+1000	15.4	0.0508	31.7	0.0289853	124.525
+1250	15.4	0.0508	31.7	0.0289853	123.255
+1600	15.4	0.0508	31.7	0.0289853	121.485
+2000	15.4	0.0508	31.7	0.0289853	120.835
+2500	15.4	0.0508	31.7	0.0289853	119.945
+3150	15.4	0.0508	31.7	0.0289853	118.045
+4000	15.4	0.0508	31.7	0.0289853	115.635
+5000	15.4	0.0508	31.7	0.0289853	112.355
+6300	15.4	0.0508	31.7	0.0289853	108.185
+200	19.7	0.0508	71.3	0.0341183	118.005
+250	19.7	0.0508	71.3	0.0341183	119.115
+315	19.7	0.0508	71.3	0.0341183	121.235
+400	19.7	0.0508	71.3	0.0341183	123.865
+500	19.7	0.0508	71.3	0.0341183	126.995
+630	19.7	0.0508	71.3	0.0341183	128.365
+800	19.7	0.0508	71.3	0.0341183	124.555
+1000	19.7	0.0508	71.3	0.0341183	121.885
+1250	19.7	0.0508	71.3	0.0341183	121.485
+1600	19.7	0.0508	71.3	0.0341183	120.575
+2000	19.7	0.0508	71.3	0.0341183	120.055
+2500	19.7	0.0508	71.3	0.0341183	118.385
+3150	19.7	0.0508	71.3	0.0341183	116.225
+4000	19.7	0.0508	71.3	0.0341183	113.045
+200	19.7	0.0508	39.6	0.036484	125.974
+250	19.7	0.0508	39.6	0.036484	127.224
+315	19.7	0.0508	39.6	0.036484	129.864
+400	19.7	0.0508	39.6	0.036484	130.614
+500	19.7	0.0508	39.6	0.036484	128.444
+630	19.7	0.0508	39.6	0.036484	120.324
+800	19.7	0.0508	39.6	0.036484	119.174
+1000	19.7	0.0508	39.6	0.036484	118.904
+1250	19.7	0.0508	39.6	0.036484	118.634
+1600	19.7	0.0508	39.6	0.036484	117.604
+2000	19.7	0.0508	39.6	0.036484	117.724
+2500	19.7	0.0508	39.6	0.036484	116.184
+3150	19.7	0.0508	39.6	0.036484	113.004
+4000	19.7	0.0508	39.6	0.036484	108.684
+2500	0	0.0254	71.3	0.000400682	133.707
+3150	0	0.0254	71.3	0.000400682	137.007
+4000	0	0.0254	71.3	0.000400682	138.557
+5000	0	0.0254	71.3	0.000400682	136.837
+6300	0	0.0254	71.3	0.000400682	134.987
+8000	0	0.0254	71.3	0.000400682	129.867
+10000	0	0.0254	71.3	0.000400682	130.787
+12500	0	0.0254	71.3	0.000400682	133.207
+16000	0	0.0254	71.3	0.000400682	130.477
+20000	0	0.0254	71.3	0.000400682	123.217
+2000	0	0.0254	55.5	0.00041229	127.623
+2500	0	0.0254	55.5	0.00041229	130.073
+3150	0	0.0254	55.5	0.00041229	130.503
+4000	0	0.0254	55.5	0.00041229	133.223
+5000	0	0.0254	55.5	0.00041229	135.803
+6300	0	0.0254	55.5	0.00041229	136.103
+8000	0	0.0254	55.5	0.00041229	136.163
+10000	0	0.0254	55.5	0.00041229	134.563
+12500	0	0.0254	55.5	0.00041229	131.453
+16000	0	0.0254	55.5	0.00041229	125.683
+20000	0	0.0254	55.5	0.00041229	121.933
+1600	0	0.0254	39.6	0.000428464	124.156
+2000	0	0.0254	39.6	0.000428464	130.026
+2500	0	0.0254	39.6	0.000428464	131.836
+3150	0	0.0254	39.6	0.000428464	133.276
+4000	0	0.0254	39.6	0.000428464	135.346
+5000	0	0.0254	39.6	0.000428464	136.536
+6300	0	0.0254	39.6	0.000428464	136.826
+8000	0	0.0254	39.6	0.000428464	135.866
+10000	0	0.0254	39.6	0.000428464	133.376
+12500	0	0.0254	39.6	0.000428464	129.116
+16000	0	0.0254	39.6	0.000428464	124.986
+1000	0	0.0254	31.7	0.000439472	125.127
+1250	0	0.0254	31.7	0.000439472	127.947
+1600	0	0.0254	31.7	0.000439472	129.267
+2000	0	0.0254	31.7	0.000439472	130.697
+2500	0	0.0254	31.7	0.000439472	132.897
+3150	0	0.0254	31.7	0.000439472	135.227
+4000	0	0.0254	31.7	0.000439472	137.047
+5000	0	0.0254	31.7	0.000439472	138.607
+6300	0	0.0254	31.7	0.000439472	138.537
+8000	0	0.0254	31.7	0.000439472	137.207
+10000	0	0.0254	31.7	0.000439472	134.227
+12500	0	0.0254	31.7	0.000439472	128.977
+16000	0	0.0254	31.7	0.000439472	125.627
+2000	4.8	0.0254	71.3	0.000848633	128.398
+2500	4.8	0.0254	71.3	0.000848633	130.828
+3150	4.8	0.0254	71.3	0.000848633	133.378
+4000	4.8	0.0254	71.3	0.000848633	134.928
+5000	4.8	0.0254	71.3	0.000848633	135.468
+6300	4.8	0.0254	71.3	0.000848633	134.498
+8000	4.8	0.0254	71.3	0.000848633	131.518
+10000	4.8	0.0254	71.3	0.000848633	127.398
+12500	4.8	0.0254	71.3	0.000848633	127.688
+16000	4.8	0.0254	71.3	0.000848633	124.208
+20000	4.8	0.0254	71.3	0.000848633	119.708
+1600	4.8	0.0254	55.5	0.000873218	121.474
+2000	4.8	0.0254	55.5	0.000873218	125.054
+2500	4.8	0.0254	55.5	0.000873218	129.144
+3150	4.8	0.0254	55.5	0.000873218	132.354
+4000	4.8	0.0254	55.5	0.000873218	133.924
+5000	4.8	0.0254	55.5	0.000873218	135.484
+6300	4.8	0.0254	55.5	0.000873218	135.164
+8000	4.8	0.0254	55.5	0.000873218	132.184
+10000	4.8	0.0254	55.5	0.000873218	126.944
+12500	4.8	0.0254	55.5	0.000873218	125.094
+16000	4.8	0.0254	55.5	0.000873218	124.394
+20000	4.8	0.0254	55.5	0.000873218	121.284
+500	4.8	0.0254	39.6	0.000907475	116.366
+630	4.8	0.0254	39.6	0.000907475	118.696
+800	4.8	0.0254	39.6	0.000907475	120.766
+1000	4.8	0.0254	39.6	0.000907475	122.956
+1250	4.8	0.0254	39.6	0.000907475	125.026
+1600	4.8	0.0254	39.6	0.000907475	125.966
+2000	4.8	0.0254	39.6	0.000907475	128.916
+2500	4.8	0.0254	39.6	0.000907475	131.236
+3150	4.8	0.0254	39.6	0.000907475	133.436
+4000	4.8	0.0254	39.6	0.000907475	134.996
+5000	4.8	0.0254	39.6	0.000907475	135.426
+6300	4.8	0.0254	39.6	0.000907475	134.336
+8000	4.8	0.0254	39.6	0.000907475	131.346
+10000	4.8	0.0254	39.6	0.000907475	126.066
+500	4.8	0.0254	31.7	0.000930789	116.128
+630	4.8	0.0254	31.7	0.000930789	120.078
+800	4.8	0.0254	31.7	0.000930789	122.648
+1000	4.8	0.0254	31.7	0.000930789	125.348
+1250	4.8	0.0254	31.7	0.000930789	127.408
+1600	4.8	0.0254	31.7	0.000930789	128.718
+2000	4.8	0.0254	31.7	0.000930789	130.148
+2500	4.8	0.0254	31.7	0.000930789	132.588
+3150	4.8	0.0254	31.7	0.000930789	134.268
+4000	4.8	0.0254	31.7	0.000930789	135.328
+5000	4.8	0.0254	31.7	0.000930789	135.248
+6300	4.8	0.0254	31.7	0.000930789	132.898
+8000	4.8	0.0254	31.7	0.000930789	127.008
+630	9.5	0.0254	71.3	0.00420654	125.726
+800	9.5	0.0254	71.3	0.00420654	127.206
+1000	9.5	0.0254	71.3	0.00420654	129.556
+1250	9.5	0.0254	71.3	0.00420654	131.656
+1600	9.5	0.0254	71.3	0.00420654	133.756
+2000	9.5	0.0254	71.3	0.00420654	134.976
+2500	9.5	0.0254	71.3	0.00420654	135.956
+3150	9.5	0.0254	71.3	0.00420654	136.166
+4000	9.5	0.0254	71.3	0.00420654	134.236
+5000	9.5	0.0254	71.3	0.00420654	131.186
+6300	9.5	0.0254	71.3	0.00420654	127.246
+400	9.5	0.0254	55.5	0.0043284	120.952
+500	9.5	0.0254	55.5	0.0043284	123.082
+630	9.5	0.0254	55.5	0.0043284	125.452
+800	9.5	0.0254	55.5	0.0043284	128.082
+1000	9.5	0.0254	55.5	0.0043284	130.332
+1250	9.5	0.0254	55.5	0.0043284	132.202
+1600	9.5	0.0254	55.5	0.0043284	133.062
+2000	9.5	0.0254	55.5	0.0043284	134.052
+2500	9.5	0.0254	55.5	0.0043284	134.152
+3150	9.5	0.0254	55.5	0.0043284	133.252
+4000	9.5	0.0254	55.5	0.0043284	131.582
+5000	9.5	0.0254	55.5	0.0043284	128.412
+6300	9.5	0.0254	55.5	0.0043284	124.222
+200	9.5	0.0254	39.6	0.00449821	116.074
+250	9.5	0.0254	39.6	0.00449821	116.924
+315	9.5	0.0254	39.6	0.00449821	119.294
+400	9.5	0.0254	39.6	0.00449821	121.154
+500	9.5	0.0254	39.6	0.00449821	123.894
+630	9.5	0.0254	39.6	0.00449821	126.514
+800	9.5	0.0254	39.6	0.00449821	129.014
+1000	9.5	0.0254	39.6	0.00449821	130.374
+1250	9.5	0.0254	39.6	0.00449821	130.964
+1600	9.5	0.0254	39.6	0.00449821	131.184
+2000	9.5	0.0254	39.6	0.00449821	131.274
+2500	9.5	0.0254	39.6	0.00449821	131.234
+3150	9.5	0.0254	39.6	0.00449821	129.934
+4000	9.5	0.0254	39.6	0.00449821	127.864
+5000	9.5	0.0254	39.6	0.00449821	125.044
+6300	9.5	0.0254	39.6	0.00449821	120.324
+200	9.5	0.0254	31.7	0.00461377	119.146
+250	9.5	0.0254	31.7	0.00461377	120.136
+315	9.5	0.0254	31.7	0.00461377	122.766
+400	9.5	0.0254	31.7	0.00461377	124.756
+500	9.5	0.0254	31.7	0.00461377	126.886
+630	9.5	0.0254	31.7	0.00461377	129.006
+800	9.5	0.0254	31.7	0.00461377	130.746
+1000	9.5	0.0254	31.7	0.00461377	131.346
+1250	9.5	0.0254	31.7	0.00461377	131.446
+1600	9.5	0.0254	31.7	0.00461377	131.036
+2000	9.5	0.0254	31.7	0.00461377	130.496
+2500	9.5	0.0254	31.7	0.00461377	130.086
+3150	9.5	0.0254	31.7	0.00461377	128.536
+4000	9.5	0.0254	31.7	0.00461377	126.736
+5000	9.5	0.0254	31.7	0.00461377	124.426
+6300	9.5	0.0254	31.7	0.00461377	120.726
+250	12.7	0.0254	71.3	0.0121808	119.698
+315	12.7	0.0254	71.3	0.0121808	122.938
+400	12.7	0.0254	71.3	0.0121808	125.048
+500	12.7	0.0254	71.3	0.0121808	126.898
+630	12.7	0.0254	71.3	0.0121808	128.878
+800	12.7	0.0254	71.3	0.0121808	130.348
+1000	12.7	0.0254	71.3	0.0121808	131.698
+1250	12.7	0.0254	71.3	0.0121808	133.048
+1600	12.7	0.0254	71.3	0.0121808	134.528
+2000	12.7	0.0254	71.3	0.0121808	134.228
+2500	12.7	0.0254	71.3	0.0121808	134.058
+3150	12.7	0.0254	71.3	0.0121808	133.758
+4000	12.7	0.0254	71.3	0.0121808	131.808
+5000	12.7	0.0254	71.3	0.0121808	128.978
+6300	12.7	0.0254	71.3	0.0121808	125.398
+8000	12.7	0.0254	71.3	0.0121808	120.538
+10000	12.7	0.0254	71.3	0.0121808	114.418
+250	12.7	0.0254	39.6	0.0130253	121.547
+315	12.7	0.0254	39.6	0.0130253	123.537
+400	12.7	0.0254	39.6	0.0130253	125.527
+500	12.7	0.0254	39.6	0.0130253	127.127
+630	12.7	0.0254	39.6	0.0130253	128.867
+800	12.7	0.0254	39.6	0.0130253	130.217
+1000	12.7	0.0254	39.6	0.0130253	130.947
+1250	12.7	0.0254	39.6	0.0130253	130.777
+1600	12.7	0.0254	39.6	0.0130253	129.977
+2000	12.7	0.0254	39.6	0.0130253	129.567
+2500	12.7	0.0254	39.6	0.0130253	129.027
+3150	12.7	0.0254	39.6	0.0130253	127.847
+4000	12.7	0.0254	39.6	0.0130253	126.537
+5000	12.7	0.0254	39.6	0.0130253	125.107
+6300	12.7	0.0254	39.6	0.0130253	123.177
+8000	12.7	0.0254	39.6	0.0130253	120.607
+10000	12.7	0.0254	39.6	0.0130253	116.017
+200	17.4	0.0254	71.3	0.016104	112.506
+250	17.4	0.0254	71.3	0.016104	113.796
+315	17.4	0.0254	71.3	0.016104	115.846
+400	17.4	0.0254	71.3	0.016104	117.396
+500	17.4	0.0254	71.3	0.016104	119.806
+630	17.4	0.0254	71.3	0.016104	122.606
+800	17.4	0.0254	71.3	0.016104	124.276
+1000	17.4	0.0254	71.3	0.016104	125.816
+1250	17.4	0.0254	71.3	0.016104	126.356
+1600	17.4	0.0254	71.3	0.016104	126.406
+2000	17.4	0.0254	71.3	0.016104	126.826
+2500	17.4	0.0254	71.3	0.016104	126.746
+3150	17.4	0.0254	71.3	0.016104	126.536
+4000	17.4	0.0254	71.3	0.016104	125.586
+5000	17.4	0.0254	71.3	0.016104	123.126
+6300	17.4	0.0254	71.3	0.016104	119.916
+8000	17.4	0.0254	71.3	0.016104	115.466
+200	17.4	0.0254	55.5	0.0165706	109.951
+250	17.4	0.0254	55.5	0.0165706	110.491
+315	17.4	0.0254	55.5	0.0165706	111.911
+400	17.4	0.0254	55.5	0.0165706	115.461
+500	17.4	0.0254	55.5	0.0165706	119.621
+630	17.4	0.0254	55.5	0.0165706	122.411
+800	17.4	0.0254	55.5	0.0165706	123.091
+1000	17.4	0.0254	55.5	0.0165706	126.001
+1250	17.4	0.0254	55.5	0.0165706	129.301
+1600	17.4	0.0254	55.5	0.0165706	126.471
+2000	17.4	0.0254	55.5	0.0165706	125.261
+2500	17.4	0.0254	55.5	0.0165706	124.931
+3150	17.4	0.0254	55.5	0.0165706	124.101
+4000	17.4	0.0254	55.5	0.0165706	121.771
+5000	17.4	0.0254	55.5	0.0165706	118.941
+6300	17.4	0.0254	55.5	0.0165706	114.861
+200	17.4	0.0254	39.6	0.0172206	114.044
+250	17.4	0.0254	39.6	0.0172206	114.714
+315	17.4	0.0254	39.6	0.0172206	115.144
+400	17.4	0.0254	39.6	0.0172206	115.444
+500	17.4	0.0254	39.6	0.0172206	117.514
+630	17.4	0.0254	39.6	0.0172206	124.514
+800	17.4	0.0254	39.6	0.0172206	135.324
+1000	17.4	0.0254	39.6	0.0172206	138.274
+1250	17.4	0.0254	39.6	0.0172206	131.364
+1600	17.4	0.0254	39.6	0.0172206	127.614
+2000	17.4	0.0254	39.6	0.0172206	126.644
+2500	17.4	0.0254	39.6	0.0172206	124.154
+3150	17.4	0.0254	39.6	0.0172206	123.564
+4000	17.4	0.0254	39.6	0.0172206	122.724
+5000	17.4	0.0254	39.6	0.0172206	119.854
+200	17.4	0.0254	31.7	0.0176631	116.146
+250	17.4	0.0254	31.7	0.0176631	116.956
+315	17.4	0.0254	31.7	0.0176631	118.416
+400	17.4	0.0254	31.7	0.0176631	120.766
+500	17.4	0.0254	31.7	0.0176631	127.676
+630	17.4	0.0254	31.7	0.0176631	136.886
+800	17.4	0.0254	31.7	0.0176631	139.226
+1000	17.4	0.0254	31.7	0.0176631	131.796
+1250	17.4	0.0254	31.7	0.0176631	128.306
+1600	17.4	0.0254	31.7	0.0176631	126.846
+2000	17.4	0.0254	31.7	0.0176631	124.356
+2500	17.4	0.0254	31.7	0.0176631	124.166
+3150	17.4	0.0254	31.7	0.0176631	123.466
+4000	17.4	0.0254	31.7	0.0176631	121.996
+5000	17.4	0.0254	31.7	0.0176631	117.996
+315	22.2	0.0254	71.3	0.0214178	115.857
+400	22.2	0.0254	71.3	0.0214178	117.927
+500	22.2	0.0254	71.3	0.0214178	117.967
+630	22.2	0.0254	71.3	0.0214178	120.657
+800	22.2	0.0254	71.3	0.0214178	123.227
+1000	22.2	0.0254	71.3	0.0214178	134.247
+1250	22.2	0.0254	71.3	0.0214178	140.987
+1600	22.2	0.0254	71.3	0.0214178	131.817
+2000	22.2	0.0254	71.3	0.0214178	127.197
+2500	22.2	0.0254	71.3	0.0214178	126.097
+3150	22.2	0.0254	71.3	0.0214178	124.127
+4000	22.2	0.0254	71.3	0.0214178	123.917
+5000	22.2	0.0254	71.3	0.0214178	125.727
+6300	22.2	0.0254	71.3	0.0214178	123.127
+8000	22.2	0.0254	71.3	0.0214178	121.657
+200	22.2	0.0254	39.6	0.0229028	116.066
+250	22.2	0.0254	39.6	0.0229028	117.386
+315	22.2	0.0254	39.6	0.0229028	120.716
+400	22.2	0.0254	39.6	0.0229028	123.416
+500	22.2	0.0254	39.6	0.0229028	129.776
+630	22.2	0.0254	39.6	0.0229028	137.026
+800	22.2	0.0254	39.6	0.0229028	137.076
+1000	22.2	0.0254	39.6	0.0229028	128.416
+1250	22.2	0.0254	39.6	0.0229028	126.446
+1600	22.2	0.0254	39.6	0.0229028	122.216
+2000	22.2	0.0254	39.6	0.0229028	121.256
+2500	22.2	0.0254	39.6	0.0229028	121.306
+3150	22.2	0.0254	39.6	0.0229028	120.856
+4000	22.2	0.0254	39.6	0.0229028	119.646
+5000	22.2	0.0254	39.6	0.0229028	118.816
+630	0	0.1016	71.3	0.00121072	124.155
+800	0	0.1016	71.3	0.00121072	126.805
+1000	0	0.1016	71.3	0.00121072	128.825
+1250	0	0.1016	71.3	0.00121072	130.335
+1600	0	0.1016	71.3	0.00121072	131.725
+2000	0	0.1016	71.3	0.00121072	132.095
+2500	0	0.1016	71.3	0.00121072	132.595
+3150	0	0.1016	71.3	0.00121072	131.955
+4000	0	0.1016	71.3	0.00121072	130.935
+5000	0	0.1016	71.3	0.00121072	130.795
+6300	0	0.1016	71.3	0.00121072	129.395
+8000	0	0.1016	71.3	0.00121072	125.465
+10000	0	0.1016	71.3	0.00121072	123.305
+12500	0	0.1016	71.3	0.00121072	119.375
+630	0	0.1016	55.5	0.00131983	126.170
+800	0	0.1016	55.5	0.00131983	127.920
+1000	0	0.1016	55.5	0.00131983	129.800
+1250	0	0.1016	55.5	0.00131983	131.430
+1600	0	0.1016	55.5	0.00131983	132.050
+2000	0	0.1016	55.5	0.00131983	132.540
+2500	0	0.1016	55.5	0.00131983	133.040
+3150	0	0.1016	55.5	0.00131983	131.780
+4000	0	0.1016	55.5	0.00131983	129.500
+5000	0	0.1016	55.5	0.00131983	128.360
+6300	0	0.1016	55.5	0.00131983	127.730
+8000	0	0.1016	55.5	0.00131983	124.450
+10000	0	0.1016	55.5	0.00131983	121.930
+12500	0	0.1016	55.5	0.00131983	119.910
+630	0	0.1016	39.6	0.00146332	125.401
+800	0	0.1016	39.6	0.00146332	128.401
+1000	0	0.1016	39.6	0.00146332	130.781
+1250	0	0.1016	39.6	0.00146332	132.271
+1600	0	0.1016	39.6	0.00146332	133.261
+2000	0	0.1016	39.6	0.00146332	133.251
+2500	0	0.1016	39.6	0.00146332	132.611
+3150	0	0.1016	39.6	0.00146332	130.961
+4000	0	0.1016	39.6	0.00146332	127.801
+5000	0	0.1016	39.6	0.00146332	126.021
+6300	0	0.1016	39.6	0.00146332	125.631
+8000	0	0.1016	39.6	0.00146332	122.341
+10000	0	0.1016	39.6	0.00146332	119.561
+630	0	0.1016	31.7	0.00150092	126.413
+800	0	0.1016	31.7	0.00150092	129.053
+1000	0	0.1016	31.7	0.00150092	131.313
+1250	0	0.1016	31.7	0.00150092	133.063
+1600	0	0.1016	31.7	0.00150092	133.553
+2000	0	0.1016	31.7	0.00150092	133.153
+2500	0	0.1016	31.7	0.00150092	132.003
+3150	0	0.1016	31.7	0.00150092	129.973
+4000	0	0.1016	31.7	0.00150092	126.933
+5000	0	0.1016	31.7	0.00150092	124.393
+6300	0	0.1016	31.7	0.00150092	124.253
+8000	0	0.1016	31.7	0.00150092	120.193
+10000	0	0.1016	31.7	0.00150092	115.893
+800	3.3	0.1016	71.3	0.00202822	131.074
+1000	3.3	0.1016	71.3	0.00202822	131.434
+1250	3.3	0.1016	71.3	0.00202822	132.304
+1600	3.3	0.1016	71.3	0.00202822	133.664
+2000	3.3	0.1016	71.3	0.00202822	134.034
+2500	3.3	0.1016	71.3	0.00202822	133.894
+3150	3.3	0.1016	71.3	0.00202822	132.114
+4000	3.3	0.1016	71.3	0.00202822	128.704
+5000	3.3	0.1016	71.3	0.00202822	127.054
+6300	3.3	0.1016	71.3	0.00202822	124.904
+8000	3.3	0.1016	71.3	0.00202822	121.234
+10000	3.3	0.1016	71.3	0.00202822	116.694
+630	3.3	0.1016	55.5	0.002211	126.599
+800	3.3	0.1016	55.5	0.002211	129.119
+1000	3.3	0.1016	55.5	0.002211	131.129
+1250	3.3	0.1016	55.5	0.002211	132.769
+1600	3.3	0.1016	55.5	0.002211	133.649
+2000	3.3	0.1016	55.5	0.002211	133.649
+2500	3.3	0.1016	55.5	0.002211	132.889
+3150	3.3	0.1016	55.5	0.002211	130.629
+4000	3.3	0.1016	55.5	0.002211	127.229
+5000	3.3	0.1016	55.5	0.002211	124.839
+6300	3.3	0.1016	55.5	0.002211	123.839
+8000	3.3	0.1016	55.5	0.002211	120.569
+10000	3.3	0.1016	55.5	0.002211	115.659
+630	3.3	0.1016	39.6	0.00245138	127.251
+800	3.3	0.1016	39.6	0.00245138	129.991
+1000	3.3	0.1016	39.6	0.00245138	131.971
+1250	3.3	0.1016	39.6	0.00245138	133.211
+1600	3.3	0.1016	39.6	0.00245138	133.071
+2000	3.3	0.1016	39.6	0.00245138	132.301
+2500	3.3	0.1016	39.6	0.00245138	130.791
+3150	3.3	0.1016	39.6	0.00245138	128.401
+4000	3.3	0.1016	39.6	0.00245138	124.881
+5000	3.3	0.1016	39.6	0.00245138	122.371
+6300	3.3	0.1016	39.6	0.00245138	120.851
+8000	3.3	0.1016	39.6	0.00245138	118.091
+10000	3.3	0.1016	39.6	0.00245138	115.321
+630	3.3	0.1016	31.7	0.00251435	128.952
+800	3.3	0.1016	31.7	0.00251435	131.362
+1000	3.3	0.1016	31.7	0.00251435	133.012
+1250	3.3	0.1016	31.7	0.00251435	134.022
+1600	3.3	0.1016	31.7	0.00251435	133.402
+2000	3.3	0.1016	31.7	0.00251435	131.642
+2500	3.3	0.1016	31.7	0.00251435	130.392
+3150	3.3	0.1016	31.7	0.00251435	128.252
+4000	3.3	0.1016	31.7	0.00251435	124.852
+5000	3.3	0.1016	31.7	0.00251435	122.082
+6300	3.3	0.1016	31.7	0.00251435	120.702
+8000	3.3	0.1016	31.7	0.00251435	117.432
+630	6.7	0.1016	71.3	0.00478288	131.448
+800	6.7	0.1016	71.3	0.00478288	134.478
+1000	6.7	0.1016	71.3	0.00478288	136.758
+1250	6.7	0.1016	71.3	0.00478288	137.658
+1600	6.7	0.1016	71.3	0.00478288	136.678
+2000	6.7	0.1016	71.3	0.00478288	134.568
+2500	6.7	0.1016	71.3	0.00478288	131.458
+3150	6.7	0.1016	71.3	0.00478288	124.458
+500	6.7	0.1016	55.5	0.0052139	129.343
+630	6.7	0.1016	55.5	0.0052139	133.023
+800	6.7	0.1016	55.5	0.0052139	135.953
+1000	6.7	0.1016	55.5	0.0052139	137.233
+1250	6.7	0.1016	55.5	0.0052139	136.883
+1600	6.7	0.1016	55.5	0.0052139	133.653
+2000	6.7	0.1016	55.5	0.0052139	129.653
+2500	6.7	0.1016	55.5	0.0052139	124.273
+400	6.7	0.1016	39.6	0.00578076	128.295
+500	6.7	0.1016	39.6	0.00578076	130.955
+630	6.7	0.1016	39.6	0.00578076	133.355
+800	6.7	0.1016	39.6	0.00578076	134.625
+1000	6.7	0.1016	39.6	0.00578076	134.515
+1250	6.7	0.1016	39.6	0.00578076	132.395
+1600	6.7	0.1016	39.6	0.00578076	127.375
+2000	6.7	0.1016	39.6	0.00578076	122.235
+315	6.7	0.1016	31.7	0.00592927	126.266
+400	6.7	0.1016	31.7	0.00592927	128.296
+500	6.7	0.1016	31.7	0.00592927	130.206
+630	6.7	0.1016	31.7	0.00592927	132.116
+800	6.7	0.1016	31.7	0.00592927	132.886
+1000	6.7	0.1016	31.7	0.00592927	131.636
+1250	6.7	0.1016	31.7	0.00592927	129.256
+1600	6.7	0.1016	31.7	0.00592927	124.346
+2000	6.7	0.1016	31.7	0.00592927	120.446
+200	8.9	0.1016	71.3	0.0103088	133.503
+250	8.9	0.1016	71.3	0.0103088	134.533
+315	8.9	0.1016	71.3	0.0103088	136.583
+400	8.9	0.1016	71.3	0.0103088	138.123
+500	8.9	0.1016	71.3	0.0103088	138.523
+630	8.9	0.1016	71.3	0.0103088	138.423
+800	8.9	0.1016	71.3	0.0103088	137.813
+1000	8.9	0.1016	71.3	0.0103088	135.433
+1250	8.9	0.1016	71.3	0.0103088	132.793
+1600	8.9	0.1016	71.3	0.0103088	128.763
+2000	8.9	0.1016	71.3	0.0103088	124.233
+2500	8.9	0.1016	71.3	0.0103088	123.623
+3150	8.9	0.1016	71.3	0.0103088	123.263
+4000	8.9	0.1016	71.3	0.0103088	120.243
+5000	8.9	0.1016	71.3	0.0103088	116.723
+6300	8.9	0.1016	71.3	0.0103088	117.253
+200	8.9	0.1016	39.6	0.0124596	133.420
+250	8.9	0.1016	39.6	0.0124596	134.340
+315	8.9	0.1016	39.6	0.0124596	135.380
+400	8.9	0.1016	39.6	0.0124596	135.540
+500	8.9	0.1016	39.6	0.0124596	133.790
+630	8.9	0.1016	39.6	0.0124596	131.920
+800	8.9	0.1016	39.6	0.0124596	130.940
+1000	8.9	0.1016	39.6	0.0124596	129.580
+1250	8.9	0.1016	39.6	0.0124596	127.710
+1600	8.9	0.1016	39.6	0.0124596	123.820
+2000	8.9	0.1016	39.6	0.0124596	119.040
+2500	8.9	0.1016	39.6	0.0124596	119.190
+3150	8.9	0.1016	39.6	0.0124596	119.350
+4000	8.9	0.1016	39.6	0.0124596	116.220
+5000	8.9	0.1016	39.6	0.0124596	113.080
+6300	8.9	0.1016	39.6	0.0124596	113.110
+200	12.3	0.1016	71.3	0.0337792	130.588
+250	12.3	0.1016	71.3	0.0337792	131.568
+315	12.3	0.1016	71.3	0.0337792	137.068
+400	12.3	0.1016	71.3	0.0337792	139.428
+500	12.3	0.1016	71.3	0.0337792	140.158
+630	12.3	0.1016	71.3	0.0337792	135.368
+800	12.3	0.1016	71.3	0.0337792	127.318
+1000	12.3	0.1016	71.3	0.0337792	127.928
+1250	12.3	0.1016	71.3	0.0337792	126.648
+1600	12.3	0.1016	71.3	0.0337792	124.748
+2000	12.3	0.1016	71.3	0.0337792	122.218
+2500	12.3	0.1016	71.3	0.0337792	121.318
+3150	12.3	0.1016	71.3	0.0337792	120.798
+4000	12.3	0.1016	71.3	0.0337792	118.018
+5000	12.3	0.1016	71.3	0.0337792	116.108
+6300	12.3	0.1016	71.3	0.0337792	113.958
+200	12.3	0.1016	55.5	0.0368233	132.304
+250	12.3	0.1016	55.5	0.0368233	133.294
+315	12.3	0.1016	55.5	0.0368233	135.674
+400	12.3	0.1016	55.5	0.0368233	136.414
+500	12.3	0.1016	55.5	0.0368233	133.774
+630	12.3	0.1016	55.5	0.0368233	124.244
+800	12.3	0.1016	55.5	0.0368233	125.114
+1000	12.3	0.1016	55.5	0.0368233	125.484
+1250	12.3	0.1016	55.5	0.0368233	124.214
+1600	12.3	0.1016	55.5	0.0368233	121.824
+2000	12.3	0.1016	55.5	0.0368233	118.564
+2500	12.3	0.1016	55.5	0.0368233	117.054
+3150	12.3	0.1016	55.5	0.0368233	116.914
+4000	12.3	0.1016	55.5	0.0368233	114.404
+5000	12.3	0.1016	55.5	0.0368233	112.014
+6300	12.3	0.1016	55.5	0.0368233	110.124
+200	12.3	0.1016	39.6	0.0408268	128.545
+250	12.3	0.1016	39.6	0.0408268	129.675
+315	12.3	0.1016	39.6	0.0408268	129.415
+400	12.3	0.1016	39.6	0.0408268	128.265
+500	12.3	0.1016	39.6	0.0408268	122.205
+630	12.3	0.1016	39.6	0.0408268	121.315
+800	12.3	0.1016	39.6	0.0408268	122.315
+1000	12.3	0.1016	39.6	0.0408268	122.435
+1250	12.3	0.1016	39.6	0.0408268	121.165
+1600	12.3	0.1016	39.6	0.0408268	117.875
+2000	12.3	0.1016	39.6	0.0408268	114.085
+2500	12.3	0.1016	39.6	0.0408268	113.315
+3150	12.3	0.1016	39.6	0.0408268	113.055
+4000	12.3	0.1016	39.6	0.0408268	110.905
+5000	12.3	0.1016	39.6	0.0408268	108.625
+6300	12.3	0.1016	39.6	0.0408268	107.985
+200	12.3	0.1016	31.7	0.0418756	124.987
+250	12.3	0.1016	31.7	0.0418756	125.857
+315	12.3	0.1016	31.7	0.0418756	124.717
+400	12.3	0.1016	31.7	0.0418756	123.207
+500	12.3	0.1016	31.7	0.0418756	118.667
+630	12.3	0.1016	31.7	0.0418756	119.287
+800	12.3	0.1016	31.7	0.0418756	120.037
+1000	12.3	0.1016	31.7	0.0418756	119.777
+1250	12.3	0.1016	31.7	0.0418756	118.767
+1600	12.3	0.1016	31.7	0.0418756	114.477
+2000	12.3	0.1016	31.7	0.0418756	110.447
+2500	12.3	0.1016	31.7	0.0418756	110.317
+3150	12.3	0.1016	31.7	0.0418756	110.307
+4000	12.3	0.1016	31.7	0.0418756	108.407
+5000	12.3	0.1016	31.7	0.0418756	107.147
+6300	12.3	0.1016	31.7	0.0418756	107.267
+200	15.6	0.1016	71.3	0.0437259	130.898
+250	15.6	0.1016	71.3	0.0437259	132.158
+315	15.6	0.1016	71.3	0.0437259	133.808
+400	15.6	0.1016	71.3	0.0437259	134.058
+500	15.6	0.1016	71.3	0.0437259	130.638
+630	15.6	0.1016	71.3	0.0437259	122.288
+800	15.6	0.1016	71.3	0.0437259	124.188
+1000	15.6	0.1016	71.3	0.0437259	124.438
+1250	15.6	0.1016	71.3	0.0437259	123.178
+1600	15.6	0.1016	71.3	0.0437259	121.528
+2000	15.6	0.1016	71.3	0.0437259	119.888
+2500	15.6	0.1016	71.3	0.0437259	118.998
+3150	15.6	0.1016	71.3	0.0437259	116.468
+4000	15.6	0.1016	71.3	0.0437259	113.298
+200	15.6	0.1016	39.6	0.0528487	123.514
+250	15.6	0.1016	39.6	0.0528487	124.644
+315	15.6	0.1016	39.6	0.0528487	122.754
+400	15.6	0.1016	39.6	0.0528487	120.484
+500	15.6	0.1016	39.6	0.0528487	115.304
+630	15.6	0.1016	39.6	0.0528487	118.084
+800	15.6	0.1016	39.6	0.0528487	118.964
+1000	15.6	0.1016	39.6	0.0528487	119.224
+1250	15.6	0.1016	39.6	0.0528487	118.214
+1600	15.6	0.1016	39.6	0.0528487	114.554
+2000	15.6	0.1016	39.6	0.0528487	110.894
+2500	15.6	0.1016	39.6	0.0528487	110.264
+3150	15.6	0.1016	39.6	0.0528487	109.254
+4000	15.6	0.1016	39.6	0.0528487	106.604
+5000	15.6	0.1016	39.6	0.0528487	106.224
+6300	15.6	0.1016	39.6	0.0528487	104.204

datasets/conditions_based_maintenance/Features.txt

@@ -0,0 +1,18 @@
+1 - Lever position (lp) [ ]
+2 - Ship speed (v) [knots]
+3 - Gas Turbine shaft torque (GTT) [kN m]
+4 - Gas Turbine rate of revolutions (GTn) [rpm]
+5 - Gas Generator rate of revolutions (GGn) [rpm]
+6 - Starboard Propeller Torque (Ts) [kN]
+7 - Port Propeller Torque (Tp) [kN]
+8 - HP Turbine exit temperature (T48) [C]
+9 - GT Compressor inlet air temperature (T1) [C]
+10 - GT Compressor outlet air temperature (T2) [C]
+11 - HP Turbine exit pressure (P48) [bar]
+12 - GT Compressor inlet air pressure (P1) [bar]
+13 - GT Compressor outlet air pressure (P2) [bar]
+14 - Gas Turbine exhaust gas pressure (Pexh) [bar]
+15 - Turbine Injecton Control (TIC) [%]
+16 - Fuel flow (mf) [kg/s]
+17 - GT Compressor decay state coefficient.
+18 - GT Turbine decay state coefficient. 

datasets/conditions_based_maintenance/README.txt

@@ -0,0 +1,92 @@
+=================================================================================================================
+Human Activity Recognition Using Smartphones Dataset
+Version 1.0
+=================================================================================================================
+Andrea Coraddu(2), Luca Oneto(1), Alessandro Ghio(1), Stefano Savio(2), Davide Anguita(1), Massimo Figari(2)
+1 - Smartlab - Non-Linear Complex Systems Laboratory
+DITEN - Universit�  degli Studi di Genova, Genoa (I-16145), Italy. 
+{luca.oneto,alessandro.ghio,davide.anguita}@unige.it
+2 - Marine Technology Research Team
+DITEN - Universit�  degli Studi di Genova, Genoa (I-16145), Italy. 
+{andrea.coraddu,stefano.savio,massimo.figari}@unige.it
+=================================================================================================================
+
+The experiments have been carried out by means of a numerical simulator of a naval vessel (Frigate) characterized by a Gas Turbine (GT) propulsion plant. The different blocks forming the complete simulator (Propeller, Hull, GT, Gear Box and Controller) have been developed and fine tuned over the year on several similar real propulsion plants. In view of these observations the available data are in agreement with a possible real vessel.
+In this release of the simulator it is also possible to take into account the performance decay over time of the GT components such as GT compressor and turbines.
+The propulsion system behaviour has been described with this parameters:
+- Ship speed (linear function of the lever position lp).
+- Compressor degradation coefficient kMc.
+- Turbine degradation coefficient kMt.
+so that each possible degradation state can be described by a combination of this triple (lp,kMt,kMc).
+The range of decay of compressor and turbine has been sampled with an uniform grid of precision 0.001 so to have a good granularity of representation.
+In particular for the compressor decay state discretization the kMc coefficient has been investigated in the domain [1; 0.95], and the turbine coefficient in the domain [1; 0.975].
+Ship speed has been investigated sampling the range of feasible speed from 3 knots to 27 knots with a granularity of representation equal to tree knots.
+A series of measures (16 features) which indirectly represents of the state of the system subject to performance decay has been acquired and stored in the dataset over the parameter's space.
+Check the README.txt file for further details about this dataset.
+
+For each record it is provided:
+======================================
+
+- A 16-feature vector containing the GT measures at steady state of the physical asset:
+    Lever position (lp) [ ]
+    Ship speed (v) [knots]
+    Gas Turbine (GT) shaft torque (GTT) [kN m]
+    GT rate of revolutions (GTn) [rpm]
+    Gas Generator rate of revolutions (GGn) [rpm]
+    Starboard Propeller Torque (Ts) [kN]
+    Port Propeller Torque (Tp) [kN]
+    Hight Pressure (HP) Turbine exit temperature (T48) [C]
+    GT Compressor inlet air temperature (T1) [C]
+    GT Compressor outlet air temperature (T2) [C]
+    HP Turbine exit pressure (P48) [bar]
+    GT Compressor inlet air pressure (P1) [bar]
+    GT Compressor outlet air pressure (P2) [bar]
+    GT exhaust gas pressure (Pexh) [bar]
+    Turbine Injecton Control (TIC) [%]
+    Fuel flow (mf) [kg/s]
+- GT Compressor decay state coefficient
+- GT Turbine decay state coefficient
+
+The dataset includes the following files:
+=========================================
+
+- 'README.txt'
+
+- 'Features.txt': List of all features.
+
+- 'data.txt': Dataset.
+
+Notes: 
+======
+- Features are not normalized
+- Each feature vector is a row on the text file (18 elements in each row)
+
+For more information about this dataset please contact: cbm@smartlab.ws
+Check at www.cbm.smartlab.ws for updates on this dataset.
+
+License:
+========
+Use of this dataset in publications must be acknowledged by referencing the following publication [1] 
+
+[1] A. Coraddu, L. Oneto, A. Ghio, S. Savio, D. Anguita, M. Figari, Machine Learning Approaches for Improving Condition?Based Maintenance of Naval Propulsion Plants, Journal of Engineering for the Maritime Environment, 2014, DOI: 10.1177/1475090214540874, (In Press)
+
+@article{Coraddu2013Machine,
+    author={Coraddu, Andrea and Oneto, Luca and Ghio, Alessandro and 
+                 Savio, Stefano and Anguita, Davide and Figari, Massimo},
+    title={Machine Learning Approaches for Improving Condition?Based Maintenance of Naval Propulsion Plants},
+    journal={Journal of Engineering for the Maritime Environment},
+    volume={--},
+    number={--},
+    pages={--},
+    year={2014}
+}
+
+This dataset is distributed AS-IS and no responsibility implied or explicit can be addressed to the authors or their institutions for its use or misuse. Any commercial use is prohibited.
+
+Other Related Publications:
+===========================
+
+[2] M. Altosole, G. Benvenuto, M. Figari, U. Campora, Real-time simulation of a cogag naval ship propulsion system, Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment 223 (1) (2009) 47-62.
+
+=================================================================================================================
+Andrea Coraddu, Luca Oneto, Alessandro Ghio, Stefano Savio, Davide Anguita, Massimo Figari. September 2014.

datasets/energy efficiency.csv

@@ -0,0 +1,769 @@
+X1,X2,X3,X4,X5,X6,X7,X8,Y1,Y2
+0.98,514.5,294,110.25,7,2,0,0,15.55,21.33
+0.98,514.5,294,110.25,7,3,0,0,15.55,21.33
+0.98,514.5,294,110.25,7,4,0,0,15.55,21.33
+0.98,514.5,294,110.25,7,5,0,0,15.55,21.33
+0.9,563.5,318.5,122.5,7,2,0,0,20.84,28.28
+0.9,563.5,318.5,122.5,7,3,0,0,21.46,25.38
+0.9,563.5,318.5,122.5,7,4,0,0,20.71,25.16
+0.9,563.5,318.5,122.5,7,5,0,0,19.68,29.6
+0.86,588,294,147,7,2,0,0,19.5,27.3
+0.86,588,294,147,7,3,0,0,19.95,21.97
+0.86,588,294,147,7,4,0,0,19.34,23.49
+0.86,588,294,147,7,5,0,0,18.31,27.87
+0.82,612.5,318.5,147,7,2,0,0,17.05,23.77
+0.82,612.5,318.5,147,7,3,0,0,17.41,21.46
+0.82,612.5,318.5,147,7,4,0,0,16.95,21.16
+0.82,612.5,318.5,147,7,5,0,0,15.98,24.93
+0.79,637,343,147,7,2,0,0,28.52,37.73
+0.79,637,343,147,7,3,0,0,29.9,31.27
+0.79,637,343,147,7,4,0,0,29.63,30.93
+0.79,637,343,147,7,5,0,0,28.75,39.44
+0.76,661.5,416.5,122.5,7,2,0,0,24.77,29.79
+0.76,661.5,416.5,122.5,7,3,0,0,23.93,29.68
+0.76,661.5,416.5,122.5,7,4,0,0,24.77,29.79
+0.76,661.5,416.5,122.5,7,5,0,0,23.93,29.4
+0.74,686,245,220.5,3.5,2,0,0,6.07,10.9
+0.74,686,245,220.5,3.5,3,0,0,6.05,11.19
+0.74,686,245,220.5,3.5,4,0,0,6.01,10.94
+0.74,686,245,220.5,3.5,5,0,0,6.04,11.17
+0.71,710.5,269.5,220.5,3.5,2,0,0,6.37,11.27
+0.71,710.5,269.5,220.5,3.5,3,0,0,6.4,11.72
+0.71,710.5,269.5,220.5,3.5,4,0,0,6.366,11.29
+0.71,710.5,269.5,220.5,3.5,5,0,0,6.4,11.67
+0.69,735,294,220.5,3.5,2,0,0,6.85,11.74
+0.69,735,294,220.5,3.5,3,0,0,6.79,12.05
+0.69,735,294,220.5,3.5,4,0,0,6.77,11.73
+0.69,735,294,220.5,3.5,5,0,0,6.81,11.93
+0.66,759.5,318.5,220.5,3.5,2,0,0,7.18,12.4
+0.66,759.5,318.5,220.5,3.5,3,0,0,7.1,12.23
+0.66,759.5,318.5,220.5,3.5,4,0,0,7.1,12.4
+0.66,759.5,318.5,220.5,3.5,5,0,0,7.1,12.14
+0.64,784,343,220.5,3.5,2,0,0,10.85,16.78
+0.64,784,343,220.5,3.5,3,0,0,10.54,16.8
+0.64,784,343,220.5,3.5,4,0,0,10.77,16.75
+0.64,784,343,220.5,3.5,5,0,0,10.56,16.67
+0.62,808.5,367.5,220.5,3.5,2,0,0,8.6,12.07
+0.62,808.5,367.5,220.5,3.5,3,0,0,8.49,12.22
+0.62,808.5,367.5,220.5,3.5,4,0,0,8.45,12.08
+0.62,808.5,367.5,220.5,3.5,5,0,0,8.5,12.04
+0.98,514.5,294,110.25,7,2,0.1,1,24.58,26.47
+0.98,514.5,294,110.25,7,3,0.1,1,24.63,26.37
+0.98,514.5,294,110.25,7,4,0.1,1,24.63,26.44
+0.98,514.5,294,110.25,7,5,0.1,1,24.59,26.29
+0.9,563.5,318.5,122.5,7,2,0.1,1,29.03,32.92
+0.9,563.5,318.5,122.5,7,3,0.1,1,29.87,29.87
+0.9,563.5,318.5,122.5,7,4,0.1,1,29.14,29.58
+0.9,563.5,318.5,122.5,7,5,0.1,1,28.09,34.33
+0.86,588,294,147,7,2,0.1,1,26.28,30.89
+0.86,588,294,147,7,3,0.1,1,26.91,25.6
+0.86,588,294,147,7,4,0.1,1,26.37,27.03
+0.86,588,294,147,7,5,0.1,1,25.27,31.73
+0.82,612.5,318.5,147,7,2,0.1,1,23.53,27.31
+0.82,612.5,318.5,147,7,3,0.1,1,24.03,24.91
+0.82,612.5,318.5,147,7,4,0.1,1,23.54,24.61
+0.82,612.5,318.5,147,7,5,0.1,1,22.58,28.51
+0.79,637,343,147,7,2,0.1,1,35.56,41.68
+0.79,637,343,147,7,3,0.1,1,37.12,35.28
+0.79,637,343,147,7,4,0.1,1,36.9,34.43
+0.79,637,343,147,7,5,0.1,1,35.94,43.33
+0.76,661.5,416.5,122.5,7,2,0.1,1,32.96,33.87
+0.76,661.5,416.5,122.5,7,3,0.1,1,32.12,34.07
+0.76,661.5,416.5,122.5,7,4,0.1,1,32.94,34.14
+0.76,661.5,416.5,122.5,7,5,0.1,1,32.21,33.67
+0.74,686,245,220.5,3.5,2,0.1,1,10.36,13.43
+0.74,686,245,220.5,3.5,3,0.1,1,10.43,13.71
+0.74,686,245,220.5,3.5,4,0.1,1,10.36,13.48
+0.74,686,245,220.5,3.5,5,0.1,1,10.39,13.7
+0.71,710.5,269.5,220.5,3.5,2,0.1,1,10.71,13.8
+0.71,710.5,269.5,220.5,3.5,3,0.1,1,10.8,14.28
+0.71,710.5,269.5,220.5,3.5,4,0.1,1,10.7,13.87
+0.71,710.5,269.5,220.5,3.5,5,0.1,1,10.75,14.27
+0.69,735,294,220.5,3.5,2,0.1,1,11.11,14.28
+0.69,735,294,220.5,3.5,3,0.1,1,11.13,14.61
+0.69,735,294,220.5,3.5,4,0.1,1,11.09,14.3
+0.69,735,294,220.5,3.5,5,0.1,1,11.16,14.45
+0.66,759.5,318.5,220.5,3.5,2,0.1,1,11.68,13.9
+0.66,759.5,318.5,220.5,3.5,3,0.1,1,11.69,13.72
+0.66,759.5,318.5,220.5,3.5,4,0.1,1,11.7,13.88
+0.66,759.5,318.5,220.5,3.5,5,0.1,1,11.69,13.65
+0.64,784,343,220.5,3.5,2,0.1,1,15.41,19.37
+0.64,784,343,220.5,3.5,3,0.1,1,15.2,19.43
+0.64,784,343,220.5,3.5,4,0.1,1,15.42,19.34
+0.64,784,343,220.5,3.5,5,0.1,1,15.21,19.32
+0.62,808.5,367.5,220.5,3.5,2,0.1,1,12.96,14.34
+0.62,808.5,367.5,220.5,3.5,3,0.1,1,12.97,14.5
+0.62,808.5,367.5,220.5,3.5,4,0.1,1,12.93,14.33
+0.62,808.5,367.5,220.5,3.5,5,0.1,1,13.02,14.27
+0.98,514.5,294,110.25,7,2,0.1,2,24.29,25.95
+0.98,514.5,294,110.25,7,3,0.1,2,24.31,25.63
+0.98,514.5,294,110.25,7,4,0.1,2,24.13,26.13
+0.98,514.5,294,110.25,7,5,0.1,2,24.25,25.89
+0.9,563.5,318.5,122.5,7,2,0.1,2,28.88,32.54
+0.9,563.5,318.5,122.5,7,3,0.1,2,29.68,29.44
+0.9,563.5,318.5,122.5,7,4,0.1,2,28.83,29.36
+0.9,563.5,318.5,122.5,7,5,0.1,2,27.9,34.2
+0.86,588,294,147,7,2,0.1,2,26.48,30.91
+0.86,588,294,147,7,3,0.1,2,27.02,25.63
+0.86,588,294,147,7,4,0.1,2,26.33,27.36
+0.86,588,294,147,7,5,0.1,2,25.36,31.9
+0.82,612.5,318.5,147,7,2,0.1,2,23.75,27.38
+0.82,612.5,318.5,147,7,3,0.1,2,24.23,25.02
+0.82,612.5,318.5,147,7,4,0.1,2,23.67,24.8
+0.82,612.5,318.5,147,7,5,0.1,2,22.79,28.79
+0.79,637,343,147,7,2,0.1,2,35.65,41.07
+0.79,637,343,147,7,3,0.1,2,37.26,34.62
+0.79,637,343,147,7,4,0.1,2,36.97,33.87
+0.79,637,343,147,7,5,0.1,2,36.03,42.86
+0.76,661.5,416.5,122.5,7,2,0.1,2,33.16,33.91
+0.76,661.5,416.5,122.5,7,3,0.1,2,32.4,34.07
+0.76,661.5,416.5,122.5,7,4,0.1,2,33.12,34.17
+0.76,661.5,416.5,122.5,7,5,0.1,2,32.41,33.78
+0.74,686,245,220.5,3.5,2,0.1,2,10.42,13.39
+0.74,686,245,220.5,3.5,3,0.1,2,10.46,13.72
+0.74,686,245,220.5,3.5,4,0.1,2,10.32,13.57
+0.74,686,245,220.5,3.5,5,0.1,2,10.45,13.79
+0.71,710.5,269.5,220.5,3.5,2,0.1,2,10.64,13.67
+0.71,710.5,269.5,220.5,3.5,3,0.1,2,10.72,14.11
+0.71,710.5,269.5,220.5,3.5,4,0.1,2,10.55,13.8
+0.71,710.5,269.5,220.5,3.5,5,0.1,2,10.68,14.21
+0.69,735,294,220.5,3.5,2,0.1,2,11.45,13.2
+0.69,735,294,220.5,3.5,3,0.1,2,11.46,13.54
+0.69,735,294,220.5,3.5,4,0.1,2,11.32,13.32
+0.69,735,294,220.5,3.5,5,0.1,2,11.49,13.51
+0.66,759.5,318.5,220.5,3.5,2,0.1,2,11.45,14.86
+0.66,759.5,318.5,220.5,3.5,3,0.1,2,11.42,14.75
+0.66,759.5,318.5,220.5,3.5,4,0.1,2,11.33,15
+0.66,759.5,318.5,220.5,3.5,5,0.1,2,11.43,14.74
+0.64,784,343,220.5,3.5,2,0.1,2,15.41,19.23
+0.64,784,343,220.5,3.5,3,0.1,2,15.18,19.34
+0.64,784,343,220.5,3.5,4,0.1,2,15.34,19.32
+0.64,784,343,220.5,3.5,5,0.1,2,15.19,19.3
+0.62,808.5,367.5,220.5,3.5,2,0.1,2,12.88,14.37
+0.62,808.5,367.5,220.5,3.5,3,0.1,2,13,14.57
+0.62,808.5,367.5,220.5,3.5,4,0.1,2,12.97,14.27
+0.62,808.5,367.5,220.5,3.5,5,0.1,2,13.04,14.24
+0.98,514.5,294,110.25,7,2,0.1,3,24.28,25.68
+0.98,514.5,294,110.25,7,3,0.1,3,24.4,26.02
+0.98,514.5,294,110.25,7,4,0.1,3,24.11,25.84
+0.98,514.5,294,110.25,7,5,0.1,3,24.35,26.14
+0.9,563.5,318.5,122.5,7,2,0.1,3,28.07,34.14
+0.9,563.5,318.5,122.5,7,3,0.1,3,29.01,32.85
+0.9,563.5,318.5,122.5,7,4,0.1,3,29.62,30.08
+0.9,563.5,318.5,122.5,7,5,0.1,3,29.05,29.67
+0.86,588,294,147,7,2,0.1,3,25.41,31.73
+0.86,588,294,147,7,3,0.1,3,26.47,31.01
+0.86,588,294,147,7,4,0.1,3,26.89,25.9
+0.86,588,294,147,7,5,0.1,3,26.46,27.4
+0.82,612.5,318.5,147,7,2,0.1,3,22.93,28.68
+0.82,612.5,318.5,147,7,3,0.1,3,23.84,27.54
+0.82,612.5,318.5,147,7,4,0.1,3,24.17,25.35
+0.82,612.5,318.5,147,7,5,0.1,3,23.87,24.93
+0.79,637,343,147,7,2,0.1,3,35.78,43.12
+0.79,637,343,147,7,3,0.1,3,35.48,41.22
+0.79,637,343,147,7,4,0.1,3,36.97,35.1
+0.79,637,343,147,7,5,0.1,3,36.7,34.29
+0.76,661.5,416.5,122.5,7,2,0.1,3,32.52,33.85
+0.76,661.5,416.5,122.5,7,3,0.1,3,33.28,34.11
+0.76,661.5,416.5,122.5,7,4,0.1,3,32.33,34.48
+0.76,661.5,416.5,122.5,7,5,0.1,3,33.24,34.5
+0.74,686,245,220.5,3.5,2,0.1,3,10.39,13.6
+0.74,686,245,220.5,3.5,3,0.1,3,10.34,13.36
+0.74,686,245,220.5,3.5,4,0.1,3,10.35,13.65
+0.74,686,245,220.5,3.5,5,0.1,3,10.38,13.49
+0.71,710.5,269.5,220.5,3.5,2,0.1,3,10.77,14.14
+0.71,710.5,269.5,220.5,3.5,3,0.1,3,10.68,13.77
+0.71,710.5,269.5,220.5,3.5,4,0.1,3,10.68,14.3
+0.71,710.5,269.5,220.5,3.5,5,0.1,3,10.7,13.87
+0.69,735,294,220.5,3.5,2,0.1,3,11.22,14.44
+0.69,735,294,220.5,3.5,3,0.1,3,11.16,14.27
+0.69,735,294,220.5,3.5,4,0.1,3,11.1,14.67
+0.69,735,294,220.5,3.5,5,0.1,3,11.14,14.4
+0.66,759.5,318.5,220.5,3.5,2,0.1,3,11.59,13.46
+0.66,759.5,318.5,220.5,3.5,3,0.1,3,11.6,13.7
+0.66,759.5,318.5,220.5,3.5,4,0.1,3,11.53,13.59
+0.66,759.5,318.5,220.5,3.5,5,0.1,3,11.61,13.83
+0.64,784,343,220.5,3.5,2,0.1,3,15.16,19.14
+0.64,784,343,220.5,3.5,3,0.1,3,15.36,19.18
+0.64,784,343,220.5,3.5,4,0.1,3,15.12,19.37
+0.64,784,343,220.5,3.5,5,0.1,3,15.36,19.29
+0.62,808.5,367.5,220.5,3.5,2,0.1,3,12.68,14.09
+0.62,808.5,367.5,220.5,3.5,3,0.1,3,12.63,14.23
+0.62,808.5,367.5,220.5,3.5,4,0.1,3,12.71,14.14
+0.62,808.5,367.5,220.5,3.5,5,0.1,3,12.73,13.89
+0.98,514.5,294,110.25,7,2,0.1,4,24.38,25.91
+0.98,514.5,294,110.25,7,3,0.1,4,24.23,25.72
+0.98,514.5,294,110.25,7,4,0.1,4,24.04,26.18
+0.98,514.5,294,110.25,7,5,0.1,4,24.32,25.87
+0.9,563.5,318.5,122.5,7,2,0.1,4,29.06,29.34
+0.9,563.5,318.5,122.5,7,3,0.1,4,28.05,33.91
+0.9,563.5,318.5,122.5,7,4,0.1,4,28.86,32.83
+0.9,563.5,318.5,122.5,7,5,0.1,4,29.79,29.92
+0.86,588,294,147,7,2,0.1,4,26.44,27.17
+0.86,588,294,147,7,3,0.1,4,25.37,31.76
+0.86,588,294,147,7,4,0.1,4,26.33,31.06
+0.86,588,294,147,7,5,0.1,4,27.03,25.81
+0.82,612.5,318.5,147,7,2,0.1,4,23.8,24.61
+0.82,612.5,318.5,147,7,3,0.1,4,22.8,28.61
+0.82,612.5,318.5,147,7,4,0.1,4,23.59,27.57
+0.82,612.5,318.5,147,7,5,0.1,4,24.24,25.16
+0.79,637,343,147,7,2,0.1,4,36.86,34.25
+0.79,637,343,147,7,3,0.1,4,35.89,43.3
+0.79,637,343,147,7,4,0.1,4,35.45,41.86
+0.79,637,343,147,7,5,0.1,4,37.1,35.29
+0.76,661.5,416.5,122.5,7,2,0.1,4,33.08,34.11
+0.76,661.5,416.5,122.5,7,3,0.1,4,32.38,33.62
+0.76,661.5,416.5,122.5,7,4,0.1,4,33.09,33.89
+0.76,661.5,416.5,122.5,7,5,0.1,4,32.31,34.05
+0.74,686,245,220.5,3.5,2,0.1,4,10.08,13.2
+0.74,686,245,220.5,3.5,3,0.1,4,10.15,13.36
+0.74,686,245,220.5,3.5,4,0.1,4,10.07,13.21
+0.74,686,245,220.5,3.5,5,0.1,4,10.14,13.53
+0.71,710.5,269.5,220.5,3.5,2,0.1,4,10.66,13.67
+0.71,710.5,269.5,220.5,3.5,3,0.1,4,10.68,14.12
+0.71,710.5,269.5,220.5,3.5,4,0.1,4,10.53,13.79
+0.71,710.5,269.5,220.5,3.5,5,0.1,4,10.72,14.2
+0.69,735,294,220.5,3.5,2,0.1,4,11.18,14.29
+0.69,735,294,220.5,3.5,3,0.1,4,11.22,14.49
+0.69,735,294,220.5,3.5,4,0.1,4,11.07,14.42
+0.69,735,294,220.5,3.5,5,0.1,4,11.2,14.73
+0.66,759.5,318.5,220.5,3.5,2,0.1,4,11.44,14.86
+0.66,759.5,318.5,220.5,3.5,3,0.1,4,11.42,14.67
+0.66,759.5,318.5,220.5,3.5,4,0.1,4,11.33,15
+0.66,759.5,318.5,220.5,3.5,5,0.1,4,11.43,14.83
+0.64,784,343,220.5,3.5,2,0.1,4,15.4,19.24
+0.64,784,343,220.5,3.5,3,0.1,4,15.19,19.25
+0.64,784,343,220.5,3.5,4,0.1,4,15.32,19.42
+0.64,784,343,220.5,3.5,5,0.1,4,15.16,19.48
+0.62,808.5,367.5,220.5,3.5,2,0.1,4,12.85,14.37
+0.62,808.5,367.5,220.5,3.5,3,0.1,4,13.04,14.34
+0.62,808.5,367.5,220.5,3.5,4,0.1,4,13,14.28
+0.62,808.5,367.5,220.5,3.5,5,0.1,4,13,14.47
+0.98,514.5,294,110.25,7,2,0.1,5,24.35,25.64
+0.98,514.5,294,110.25,7,3,0.1,5,24.33,25.98
+0.98,514.5,294,110.25,7,4,0.1,5,24.03,25.88
+0.98,514.5,294,110.25,7,5,0.1,5,24.26,26.18
+0.9,563.5,318.5,122.5,7,2,0.1,5,29.83,29.82
+0.9,563.5,318.5,122.5,7,3,0.1,5,29.08,29.52
+0.9,563.5,318.5,122.5,7,4,0.1,5,28.03,34.45
+0.9,563.5,318.5,122.5,7,5,0.1,5,29.02,33.01
+0.86,588,294,147,7,2,0.1,5,27.03,25.82
+0.86,588,294,147,7,3,0.1,5,26.45,27.33
+0.86,588,294,147,7,4,0.1,5,25.36,32.04
+0.86,588,294,147,7,5,0.1,5,26.45,31.28
+0.82,612.5,318.5,147,7,2,0.1,5,24.37,25.11
+0.82,612.5,318.5,147,7,3,0.1,5,23.89,24.77
+0.82,612.5,318.5,147,7,4,0.1,5,22.89,28.88
+0.82,612.5,318.5,147,7,5,0.1,5,23.86,27.69
+0.79,637,343,147,7,2,0.1,5,37.03,34.99
+0.79,637,343,147,7,3,0.1,5,36.71,34.18
+0.79,637,343,147,7,4,0.1,5,36.77,43.14
+0.79,637,343,147,7,5,0.1,5,35.48,41.26
+0.76,661.5,416.5,122.5,7,2,0.1,5,32.31,34.25
+0.76,661.5,416.5,122.5,7,3,0.1,5,33.21,34.35
+0.76,661.5,416.5,122.5,7,4,0.1,5,32.46,33.64
+0.76,661.5,416.5,122.5,7,5,0.1,5,33.27,33.88
+0.74,686,245,220.5,3.5,2,0.1,5,10.47,13.65
+0.74,686,245,220.5,3.5,3,0.1,5,10.37,13.44
+0.74,686,245,220.5,3.5,4,0.1,5,10.34,13.72
+0.74,686,245,220.5,3.5,5,0.1,5,10.39,13.5
+0.71,710.5,269.5,220.5,3.5,2,0.1,5,10.78,14.18
+0.71,710.5,269.5,220.5,3.5,3,0.1,5,10.7,13.75
+0.71,710.5,269.5,220.5,3.5,4,0.1,5,10.67,14.26
+0.71,710.5,269.5,220.5,3.5,5,0.1,5,13.69,13.89
+0.69,735,294,220.5,3.5,2,0.1,5,11.21,14.55
+0.69,735,294,220.5,3.5,3,0.1,5,11.14,14.28
+0.69,735,294,220.5,3.5,4,0.1,5,11.11,14.46
+0.69,735,294,220.5,3.5,5,0.1,5,11.16,14.39
+0.66,759.5,318.5,220.5,3.5,2,0.1,5,11.38,14.54
+0.66,759.5,318.5,220.5,3.5,3,0.1,5,11.34,14.81
+0.66,759.5,318.5,220.5,3.5,4,0.1,5,11.22,14.65
+0.66,759.5,318.5,220.5,3.5,5,0.1,5,11.34,14.87
+0.64,784,343,220.5,3.5,2,0.1,5,15.16,19.24
+0.64,784,343,220.5,3.5,3,0.1,5,15.37,19.18
+0.64,784,343,220.5,3.5,4,0.1,5,15.12,19.26
+0.64,784,343,220.5,3.5,5,0.1,5,15.36,19.29
+0.62,808.5,367.5,220.5,3.5,2,0.1,5,12.59,14.24
+0.62,808.5,367.5,220.5,3.5,3,0.1,5,12.74,13.97
+0.62,808.5,367.5,220.5,3.5,4,0.1,5,12.8,13.99
+0.62,808.5,367.5,220.5,3.5,5,0.1,5,12.62,14.15
+0.98,514.5,294,110.25,7,2,0.25,1,28.15,29.79
+0.98,514.5,294,110.25,7,3,0.25,1,28.15,29.79
+0.98,514.5,294,110.25,7,4,0.25,1,28.37,29.28
+0.98,514.5,294,110.25,7,5,0.25,1,28.41,29.49
+0.9,563.5,318.5,122.5,7,2,0.25,1,32.68,36.12
+0.9,563.5,318.5,122.5,7,3,0.25,1,33.48,33.17
+0.9,563.5,318.5,122.5,7,4,0.25,1,32.84,32.71
+0.9,563.5,318.5,122.5,7,5,0.25,1,32,37.58
+0.86,588,294,147,7,2,0.25,1,29.54,33.98
+0.86,588,294,147,7,3,0.25,1,30.05,28.61
+0.86,588,294,147,7,4,0.25,1,29.6,30.12
+0.86,588,294,147,7,5,0.25,1,28.66,34.73
+0.82,612.5,318.5,147,7,2,0.25,1,26.84,30.17
+0.82,612.5,318.5,147,7,3,0.25,1,27.27,27.84
+0.82,612.5,318.5,147,7,4,0.25,1,26.97,27.25
+0.82,612.5,318.5,147,7,5,0.25,1,26.19,31.39
+0.79,637,343,147,7,2,0.25,1,38.67,43.8
+0.79,637,343,147,7,3,0.25,1,40.03,37.81
+0.79,637,343,147,7,4,0.25,1,39.86,36.85
+0.79,637,343,147,7,5,0.25,1,39.04,45.52
+0.76,661.5,416.5,122.5,7,2,0.25,1,36.96,36.85
+0.76,661.5,416.5,122.5,7,3,0.25,1,36.13,37.58
+0.76,661.5,416.5,122.5,7,4,0.25,1,36.91,37.45
+0.76,661.5,416.5,122.5,7,5,0.25,1,36.43,36.62
+0.74,686,245,220.5,3.5,2,0.25,1,12.43,15.19
+0.74,686,245,220.5,3.5,3,0.25,1,12.5,15.5
+0.74,686,245,220.5,3.5,4,0.25,1,12.41,15.28
+0.74,686,245,220.5,3.5,5,0.25,1,12.45,15.5
+0.71,710.5,269.5,220.5,3.5,2,0.25,1,12.57,15.42
+0.71,710.5,269.5,220.5,3.5,3,0.25,1,12.65,15.85
+0.71,710.5,269.5,220.5,3.5,4,0.25,1,12.57,15.44
+0.71,710.5,269.5,220.5,3.5,5,0.25,1,12.63,15.81
+0.69,735,294,220.5,3.5,2,0.25,1,12.78,15.21
+0.69,735,294,220.5,3.5,3,0.25,1,12.93,15.63
+0.69,735,294,220.5,3.5,4,0.25,1,12.73,15.48
+0.69,735,294,220.5,3.5,5,0.25,1,12.72,15.78
+0.66,759.5,318.5,220.5,3.5,2,0.25,1,13.17,16.39
+0.66,759.5,318.5,220.5,3.5,3,0.25,1,13.18,16.27
+0.66,759.5,318.5,220.5,3.5,4,0.25,1,13.17,16.39
+0.66,759.5,318.5,220.5,3.5,5,0.25,1,13.18,16.19
+0.64,784,343,220.5,3.5,2,0.25,1,17.5,21.13
+0.64,784,343,220.5,3.5,3,0.25,1,17.35,21.19
+0.64,784,343,220.5,3.5,4,0.25,1,17.52,21.09
+0.64,784,343,220.5,3.5,5,0.25,1,17.37,21.08
+0.62,808.5,367.5,220.5,3.5,2,0.25,1,15.09,15.77
+0.62,808.5,367.5,220.5,3.5,3,0.25,1,15.12,15.95
+0.62,808.5,367.5,220.5,3.5,4,0.25,1,15.08,15.77
+0.62,808.5,367.5,220.5,3.5,5,0.25,1,15.16,15.76
+0.98,514.5,294,110.25,7,2,0.25,2,28.67,29.62
+0.98,514.5,294,110.25,7,3,0.25,2,28.57,29.69
+0.98,514.5,294,110.25,7,4,0.25,2,28.18,30.18
+0.98,514.5,294,110.25,7,5,0.25,2,28.6,30.02
+0.9,563.5,318.5,122.5,7,2,0.25,2,32.46,35.56
+0.9,563.5,318.5,122.5,7,3,0.25,2,33.27,32.64
+0.9,563.5,318.5,122.5,7,4,0.25,2,32.33,32.77
+0.9,563.5,318.5,122.5,7,5,0.25,2,31.66,37.72
+0.86,588,294,147,7,2,0.25,2,29.34,33.37
+0.86,588,294,147,7,3,0.25,2,29.87,27.89
+0.86,588,294,147,7,4,0.25,2,29.27,29.9
+0.86,588,294,147,7,5,0.25,2,28.4,34.52
+0.82,612.5,318.5,147,7,2,0.25,2,25.74,28.27
+0.82,612.5,318.5,147,7,3,0.25,2,25.98,26.96
+0.82,612.5,318.5,147,7,4,0.25,2,25.38,26.72
+0.82,612.5,318.5,147,7,5,0.25,2,24.94,29.88
+0.79,637,343,147,7,2,0.25,2,38.57,43.86
+0.79,637,343,147,7,3,0.25,2,40.19,37.41
+0.79,637,343,147,7,4,0.25,2,39.97,36.77
+0.79,637,343,147,7,5,0.25,2,38.98,45.97
+0.76,661.5,416.5,122.5,7,2,0.25,2,36.95,36.87
+0.76,661.5,416.5,122.5,7,3,0.25,2,36.28,37.35
+0.76,661.5,416.5,122.5,7,4,0.25,2,36.86,37.28
+0.76,661.5,416.5,122.5,7,5,0.25,2,36.45,36.81
+0.74,686,245,220.5,3.5,2,0.25,2,12.35,14.73
+0.74,686,245,220.5,3.5,3,0.25,2,12.45,15.1
+0.74,686,245,220.5,3.5,4,0.25,2,12.16,15.18
+0.74,686,245,220.5,3.5,5,0.25,2,12.3,15.44
+0.71,710.5,269.5,220.5,3.5,2,0.25,2,12.33,14.91
+0.71,710.5,269.5,220.5,3.5,3,0.25,2,12.29,15.4
+0.71,710.5,269.5,220.5,3.5,4,0.25,2,12.2,14.94
+0.71,710.5,269.5,220.5,3.5,5,0.25,2,12.49,15.32
+0.69,735,294,220.5,3.5,2,0.25,2,12.85,15.52
+0.69,735,294,220.5,3.5,3,0.25,2,12.87,15.85
+0.69,735,294,220.5,3.5,4,0.25,2,12.73,15.66
+0.69,735,294,220.5,3.5,5,0.25,2,12.95,15.99
+0.66,759.5,318.5,220.5,3.5,2,0.25,2,13.05,15.89
+0.66,759.5,318.5,220.5,3.5,3,0.25,2,12.93,15.85
+0.66,759.5,318.5,220.5,3.5,4,0.25,2,12.77,16.22
+0.66,759.5,318.5,220.5,3.5,5,0.25,2,13,15.87
+0.64,784,343,220.5,3.5,2,0.25,2,17.14,20.47
+0.64,784,343,220.5,3.5,3,0.25,2,16.84,20.56
+0.64,784,343,220.5,3.5,4,0.25,2,17.02,20.48
+0.64,784,343,220.5,3.5,5,0.25,2,17.11,20.43
+0.62,808.5,367.5,220.5,3.5,2,0.25,2,14.34,15.32
+0.62,808.5,367.5,220.5,3.5,3,0.25,2,14.66,15.64
+0.62,808.5,367.5,220.5,3.5,4,0.25,2,14.6,15.14
+0.62,808.5,367.5,220.5,3.5,5,0.25,2,14.6,15.3
+0.98,514.5,294,110.25,7,2,0.25,3,28.67,29.43
+0.98,514.5,294,110.25,7,3,0.25,3,28.56,29.78
+0.98,514.5,294,110.25,7,4,0.25,3,28.17,30.1
+0.98,514.5,294,110.25,7,5,0.25,3,28.63,30.19
+0.9,563.5,318.5,122.5,7,2,0.25,3,31.63,36.35
+0.9,563.5,318.5,122.5,7,3,0.25,3,32.4,35.1
+0.9,563.5,318.5,122.5,7,4,0.25,3,32.68,32.83
+0.9,563.5,318.5,122.5,7,5,0.25,3,32.29,32.46
+0.86,588,294,147,7,2,0.25,3,28.4,33.52
+0.86,588,294,147,7,3,0.25,3,29.4,32.93
+0.86,588,294,147,7,4,0.25,3,29.43,28.38
+0.86,588,294,147,7,5,0.25,3,29.07,29.82
+0.82,612.5,318.5,147,7,2,0.25,3,24.7,28.77
+0.82,612.5,318.5,147,7,3,0.25,3,25.48,27.76
+0.82,612.5,318.5,147,7,4,0.25,3,25.37,26.95
+0.82,612.5,318.5,147,7,5,0.25,3,25.17,26.41
+0.79,637,343,147,7,2,0.25,3,39.04,45.13
+0.79,637,343,147,7,3,0.25,3,38.35,43.66
+0.79,637,343,147,7,4,0.25,3,39.81,37.76
+0.79,637,343,147,7,5,0.25,3,39.83,36.87
+0.76,661.5,416.5,122.5,7,2,0.25,3,35.99,36.07
+0.76,661.5,416.5,122.5,7,3,0.25,3,36.59,36.44
+0.76,661.5,416.5,122.5,7,4,0.25,3,35.64,37.28
+0.76,661.5,416.5,122.5,7,5,0.25,3,36.52,37.29
+0.74,686,245,220.5,3.5,2,0.25,3,11.8,14.49
+0.74,686,245,220.5,3.5,3,0.25,3,12.03,13.79
+0.74,686,245,220.5,3.5,4,0.25,3,11.98,14.72
+0.74,686,245,220.5,3.5,5,0.25,3,11.69,14.76
+0.71,710.5,269.5,220.5,3.5,2,0.25,3,12.41,14.92
+0.71,710.5,269.5,220.5,3.5,3,0.25,3,12.28,14.74
+0.71,710.5,269.5,220.5,3.5,4,0.25,3,12.1,15.57
+0.71,710.5,269.5,220.5,3.5,5,0.25,3,12.19,14.94
+0.69,735,294,220.5,3.5,2,0.25,3,12.34,14.92
+0.69,735,294,220.5,3.5,3,0.25,3,12.46,14.38
+0.69,735,294,220.5,3.5,4,0.25,3,12.31,15.44
+0.69,735,294,220.5,3.5,5,0.25,3,12.12,15.17
+0.66,759.5,318.5,220.5,3.5,2,0.25,3,12.97,15.53
+0.66,759.5,318.5,220.5,3.5,3,0.25,3,13.01,15.8
+0.66,759.5,318.5,220.5,3.5,4,0.25,3,12.74,16.14
+0.66,759.5,318.5,220.5,3.5,5,0.25,3,12.84,16.26
+0.64,784,343,220.5,3.5,2,0.25,3,16.83,19.87
+0.64,784,343,220.5,3.5,3,0.25,3,16.93,20.03
+0.64,784,343,220.5,3.5,4,0.25,3,16.66,20.46
+0.64,784,343,220.5,3.5,5,0.25,3,16.86,20.28
+0.62,808.5,367.5,220.5,3.5,2,0.25,3,13.91,14.89
+0.62,808.5,367.5,220.5,3.5,3,0.25,3,14.34,14.96
+0.62,808.5,367.5,220.5,3.5,4,0.25,3,13.95,14.89
+0.62,808.5,367.5,220.5,3.5,5,0.25,3,13.99,14.35
+0.98,514.5,294,110.25,7,2,0.25,4,28.7,29.61
+0.98,514.5,294,110.25,7,3,0.25,4,28.55,29.59
+0.98,514.5,294,110.25,7,4,0.25,4,28.15,30.19
+0.98,514.5,294,110.25,7,5,0.25,4,28.62,30.12
+0.9,563.5,318.5,122.5,7,2,0.25,4,32.67,32.12
+0.9,563.5,318.5,122.5,7,3,0.25,4,31.69,37.12
+0.9,563.5,318.5,122.5,7,4,0.25,4,32.07,36.16
+0.9,563.5,318.5,122.5,7,5,0.25,4,33.28,33.16
+0.86,588,294,147,7,2,0.25,4,29.47,29.45
+0.86,588,294,147,7,3,0.25,4,28.42,34.19
+0.86,588,294,147,7,4,0.25,4,29.08,33.93
+0.86,588,294,147,7,5,0.25,4,29.88,28.31
+0.82,612.5,318.5,147,7,2,0.25,4,25.66,26.3
+0.82,612.5,318.5,147,7,3,0.25,4,24.96,29.43
+0.82,612.5,318.5,147,7,4,0.25,4,25.43,28.76
+0.82,612.5,318.5,147,7,5,0.25,4,26,27.34
+0.79,637,343,147,7,2,0.25,4,40,36.26
+0.79,637,343,147,7,3,0.25,4,38.84,45.48
+0.79,637,343,147,7,4,0.25,4,38.33,44.16
+0.79,637,343,147,7,5,0.25,4,40.12,37.26
+0.76,661.5,416.5,122.5,7,2,0.25,4,36.95,37.2
+0.76,661.5,416.5,122.5,7,3,0.25,4,36.45,36.76
+0.76,661.5,416.5,122.5,7,4,0.25,4,36.81,37.05
+0.76,661.5,416.5,122.5,7,5,0.25,4,36.26,37.51
+0.74,686,245,220.5,3.5,2,0.25,4,12.32,14.92
+0.74,686,245,220.5,3.5,3,0.25,4,12.3,15.24
+0.74,686,245,220.5,3.5,4,0.25,4,12.18,15.03
+0.74,686,245,220.5,3.5,5,0.25,4,12.43,15.35
+0.71,710.5,269.5,220.5,3.5,2,0.25,4,12.36,14.67
+0.71,710.5,269.5,220.5,3.5,3,0.25,4,12.49,15.09
+0.71,710.5,269.5,220.5,3.5,4,0.25,4,12.17,15.2
+0.71,710.5,269.5,220.5,3.5,5,0.25,4,12.28,15.64
+0.69,735,294,220.5,3.5,2,0.25,4,12.91,15.37
+0.69,735,294,220.5,3.5,3,0.25,4,12.95,15.73
+0.69,735,294,220.5,3.5,4,0.25,4,12.67,15.83
+0.69,735,294,220.5,3.5,5,0.25,4,12.86,16.13
+0.66,759.5,318.5,220.5,3.5,2,0.25,4,12.95,15.95
+0.66,759.5,318.5,220.5,3.5,3,0.25,4,13,15.59
+0.66,759.5,318.5,220.5,3.5,4,0.25,4,12.86,16.17
+0.66,759.5,318.5,220.5,3.5,5,0.25,4,12.92,16.14
+0.64,784,343,220.5,3.5,2,0.25,4,16.99,19.65
+0.64,784,343,220.5,3.5,3,0.25,4,16.69,19.76
+0.64,784,343,220.5,3.5,4,0.25,4,16.56,20.37
+0.64,784,343,220.5,3.5,5,0.25,4,16.62,19.9
+0.62,808.5,367.5,220.5,3.5,2,0.25,4,14.33,15.41
+0.62,808.5,367.5,220.5,3.5,3,0.25,4,14.61,15.56
+0.62,808.5,367.5,220.5,3.5,4,0.25,4,14.61,15.07
+0.62,808.5,367.5,220.5,3.5,5,0.25,4,14.65,15.38
+0.98,514.5,294,110.25,7,2,0.25,5,28.69,29.53
+0.98,514.5,294,110.25,7,3,0.25,5,28.58,29.77
+0.98,514.5,294,110.25,7,4,0.25,5,28.15,30
+0.98,514.5,294,110.25,7,5,0.25,5,28.61,30.2
+0.9,563.5,318.5,122.5,7,2,0.25,5,33.13,32.25
+0.9,563.5,318.5,122.5,7,3,0.25,5,32.31,32
+0.9,563.5,318.5,122.5,7,4,0.25,5,31.53,37.19
+0.9,563.5,318.5,122.5,7,5,0.25,5,32.46,35.62
+0.86,588,294,147,7,2,0.25,5,29.71,28.02
+0.86,588,294,147,7,3,0.25,5,29.09,29.43
+0.86,588,294,147,7,4,0.25,5,28.31,34.15
+0.86,588,294,147,7,5,0.25,5,29.39,33.47
+0.82,612.5,318.5,147,7,2,0.25,5,25.7,26.53
+0.82,612.5,318.5,147,7,3,0.25,5,25.17,26.08
+0.82,612.5,318.5,147,7,4,0.25,5,24.6,29.31
+0.82,612.5,318.5,147,7,5,0.25,5,25.49,28.14
+0.79,637,343,147,7,2,0.25,5,39.89,37.54
+0.79,637,343,147,7,3,0.25,5,39.83,36.66
+0.79,637,343,147,7,4,0.25,5,39.01,45.28
+0.79,637,343,147,7,5,0.25,5,38.65,43.73
+0.76,661.5,416.5,122.5,7,2,0.25,5,35.69,36.93
+0.76,661.5,416.5,122.5,7,3,0.25,5,36.64,37.01
+0.76,661.5,416.5,122.5,7,4,0.25,5,36.06,35.73
+0.76,661.5,416.5,122.5,7,5,0.25,5,36.7,36.15
+0.74,686,245,220.5,3.5,2,0.25,5,12.12,14.48
+0.74,686,245,220.5,3.5,3,0.25,5,11.67,14.58
+0.74,686,245,220.5,3.5,4,0.25,5,11.64,14.81
+0.74,686,245,220.5,3.5,5,0.25,5,12.02,14.03
+0.71,710.5,269.5,220.5,3.5,2,0.25,5,12.27,15.27
+0.71,710.5,269.5,220.5,3.5,3,0.25,5,12.19,14.71
+0.71,710.5,269.5,220.5,3.5,4,0.25,5,12.25,15.23
+0.71,710.5,269.5,220.5,3.5,5,0.25,5,12.27,14.97
+0.69,735,294,220.5,3.5,2,0.25,5,12.47,15.14
+0.69,735,294,220.5,3.5,3,0.25,5,12.12,14.97
+0.69,735,294,220.5,3.5,4,0.25,5,12.18,15.22
+0.69,735,294,220.5,3.5,5,0.25,5,12.47,14.6
+0.66,759.5,318.5,220.5,3.5,2,0.25,5,12.93,15.83
+0.66,759.5,318.5,220.5,3.5,3,0.25,5,12.82,16.03
+0.66,759.5,318.5,220.5,3.5,4,0.25,5,12.78,15.8
+0.66,759.5,318.5,220.5,3.5,5,0.25,5,13.02,16.06
+0.64,784,343,220.5,3.5,2,0.25,5,16.73,20.13
+0.64,784,343,220.5,3.5,3,0.25,5,16.86,20.01
+0.64,784,343,220.5,3.5,4,0.25,5,16.76,20.19
+0.64,784,343,220.5,3.5,5,0.25,5,16.92,20.29
+0.62,808.5,367.5,220.5,3.5,2,0.25,5,13.68,15.19
+0.62,808.5,367.5,220.5,3.5,3,0.25,5,13.99,14.61
+0.62,808.5,367.5,220.5,3.5,4,0.25,5,14.16,14.61
+0.62,808.5,367.5,220.5,3.5,5,0.25,5,13.86,14.75
+0.98,514.5,294,110.25,7,2,0.4,1,32.26,33.37
+0.98,514.5,294,110.25,7,3,0.4,1,32.26,33.34
+0.98,514.5,294,110.25,7,4,0.4,1,32.49,32.83
+0.98,514.5,294,110.25,7,5,0.4,1,32.53,33.04
+0.9,563.5,318.5,122.5,7,2,0.4,1,36.47,39.28
+0.9,563.5,318.5,122.5,7,3,0.4,1,37.24,36.38
+0.9,563.5,318.5,122.5,7,4,0.4,1,36.66,35.92
+0.9,563.5,318.5,122.5,7,5,0.4,1,35.96,40.99
+0.86,588,294,147,7,2,0.4,1,31.89,35.99
+0.86,588,294,147,7,3,0.4,1,32.39,30.66
+0.86,588,294,147,7,4,0.4,1,32.09,31.7
+0.86,588,294,147,7,5,0.4,1,31.29,36.73
+0.82,612.5,318.5,147,7,2,0.4,1,29.22,31.71
+0.82,612.5,318.5,147,7,3,0.4,1,29.91,29.13
+0.82,612.5,318.5,147,7,4,0.4,1,29.53,28.99
+0.82,612.5,318.5,147,7,5,0.4,1,28.65,33.54
+0.79,637,343,147,7,2,0.4,1,41.4,45.29
+0.79,637,343,147,7,3,0.4,1,42.62,39.07
+0.79,637,343,147,7,4,0.4,1,42.5,38.35
+0.79,637,343,147,7,5,0.4,1,41.67,46.94
+0.76,661.5,416.5,122.5,7,2,0.4,1,40.78,39.55
+0.76,661.5,416.5,122.5,7,3,0.4,1,39.97,40.85
+0.76,661.5,416.5,122.5,7,4,0.4,1,40.71,40.63
+0.76,661.5,416.5,122.5,7,5,0.4,1,40.43,39.48
+0.74,686,245,220.5,3.5,2,0.4,1,14.52,16.94
+0.74,686,245,220.5,3.5,3,0.4,1,14.61,17.25
+0.74,686,245,220.5,3.5,4,0.4,1,14.5,17.03
+0.74,686,245,220.5,3.5,5,0.4,1,14.55,17.25
+0.71,710.5,269.5,220.5,3.5,2,0.4,1,14.51,17.1
+0.71,710.5,269.5,220.5,3.5,3,0.4,1,14.6,17.51
+0.71,710.5,269.5,220.5,3.5,4,0.4,1,14.5,17.12
+0.71,710.5,269.5,220.5,3.5,5,0.4,1,14.58,17.47
+0.69,735,294,220.5,3.5,2,0.4,1,14.51,16.5
+0.69,735,294,220.5,3.5,3,0.4,1,14.7,17
+0.69,735,294,220.5,3.5,4,0.4,1,14.42,16.87
+0.69,735,294,220.5,3.5,5,0.4,1,14.42,17.2
+0.66,759.5,318.5,220.5,3.5,2,0.4,1,15.23,18.14
+0.66,759.5,318.5,220.5,3.5,3,0.4,1,15.23,18.03
+0.66,759.5,318.5,220.5,3.5,4,0.4,1,15.23,18.14
+0.66,759.5,318.5,220.5,3.5,5,0.4,1,15.23,17.95
+0.64,784,343,220.5,3.5,2,0.4,1,19.52,22.72
+0.64,784,343,220.5,3.5,3,0.4,1,19.36,22.73
+0.64,784,343,220.5,3.5,4,0.4,1,19.48,22.72
+0.64,784,343,220.5,3.5,5,0.4,1,19.42,22.53
+0.62,808.5,367.5,220.5,3.5,2,0.4,1,15.09,17.2
+0.62,808.5,367.5,220.5,3.5,3,0.4,1,17.17,17.21
+0.62,808.5,367.5,220.5,3.5,4,0.4,1,17.14,17.15
+0.62,808.5,367.5,220.5,3.5,5,0.4,1,17.14,17.2
+0.98,514.5,294,110.25,7,2,0.4,2,32.82,32.96
+0.98,514.5,294,110.25,7,3,0.4,2,32.71,33.13
+0.98,514.5,294,110.25,7,4,0.4,2,32.24,33.94
+0.98,514.5,294,110.25,7,5,0.4,2,32.72,33.78
+0.9,563.5,318.5,122.5,7,2,0.4,2,35.84,38.35
+0.9,563.5,318.5,122.5,7,3,0.4,2,36.57,35.39
+0.9,563.5,318.5,122.5,7,4,0.4,2,36.06,34.94
+0.9,563.5,318.5,122.5,7,5,0.4,2,35.69,40.66
+0.86,588,294,147,7,2,0.4,2,32.48,35.48
+0.86,588,294,147,7,3,0.4,2,32.74,30.53
+0.86,588,294,147,7,4,0.4,2,32.13,32.28
+0.86,588,294,147,7,5,0.4,2,31.64,36.86
+0.82,612.5,318.5,147,7,2,0.4,2,28.95,30.34
+0.82,612.5,318.5,147,7,3,0.4,2,29.49,27.93
+0.82,612.5,318.5,147,7,4,0.4,2,28.64,28.95
+0.82,612.5,318.5,147,7,5,0.4,2,28.01,32.92
+0.79,637,343,147,7,2,0.4,2,41.64,45.59
+0.79,637,343,147,7,3,0.4,2,43.1,39.41
+0.79,637,343,147,7,4,0.4,2,42.74,38.84
+0.79,637,343,147,7,5,0.4,2,41.92,48.03
+0.76,661.5,416.5,122.5,7,2,0.4,2,40.78,39.48
+0.76,661.5,416.5,122.5,7,3,0.4,2,40.15,40.4
+0.76,661.5,416.5,122.5,7,4,0.4,2,40.57,40.47
+0.76,661.5,416.5,122.5,7,5,0.4,2,40.42,39.7
+0.74,686,245,220.5,3.5,2,0.4,2,14.54,16.43
+0.74,686,245,220.5,3.5,3,0.4,2,14.45,16.93
+0.74,686,245,220.5,3.5,4,0.4,2,14.18,16.99
+0.74,686,245,220.5,3.5,5,0.4,2,14.5,17.03
+0.71,710.5,269.5,220.5,3.5,2,0.4,2,14.7,16.77
+0.71,710.5,269.5,220.5,3.5,3,0.4,2,14.66,17.37
+0.71,710.5,269.5,220.5,3.5,4,0.4,2,14.4,17.27
+0.71,710.5,269.5,220.5,3.5,5,0.4,2,14.71,17.51
+0.69,735,294,220.5,3.5,2,0.4,2,14.75,16.44
+0.69,735,294,220.5,3.5,3,0.4,2,14.71,17.01
+0.69,735,294,220.5,3.5,4,0.4,2,14.33,17.23
+0.69,735,294,220.5,3.5,5,0.4,2,14.62,17.22
+0.66,759.5,318.5,220.5,3.5,2,0.4,2,15.34,17.85
+0.66,759.5,318.5,220.5,3.5,3,0.4,2,15.29,17.89
+0.66,759.5,318.5,220.5,3.5,4,0.4,2,15.09,18.36
+0.66,759.5,318.5,220.5,3.5,5,0.4,2,15.3,18.15
+0.64,784,343,220.5,3.5,2,0.4,2,19.2,21.72
+0.64,784,343,220.5,3.5,3,0.4,2,18.88,22.07
+0.64,784,343,220.5,3.5,4,0.4,2,18.9,22.09
+0.64,784,343,220.5,3.5,5,0.4,2,19.12,21.93
+0.62,808.5,367.5,220.5,3.5,2,0.4,2,16.76,17.36
+0.62,808.5,367.5,220.5,3.5,3,0.4,2,17.23,17.38
+0.62,808.5,367.5,220.5,3.5,4,0.4,2,17.26,16.86
+0.62,808.5,367.5,220.5,3.5,5,0.4,2,17.15,16.99
+0.98,514.5,294,110.25,7,2,0.4,3,32.82,32.78
+0.98,514.5,294,110.25,7,3,0.4,3,32.69,33.24
+0.98,514.5,294,110.25,7,4,0.4,3,32.23,33.86
+0.98,514.5,294,110.25,7,5,0.4,3,32.75,34
+0.9,563.5,318.5,122.5,7,2,0.4,3,34.24,37.26
+0.9,563.5,318.5,122.5,7,3,0.4,3,34.95,35.04
+0.9,563.5,318.5,122.5,7,4,0.4,3,35.05,33.82
+0.9,563.5,318.5,122.5,7,5,0.4,3,34.29,33.31
+0.86,588,294,147,7,2,0.4,3,31.28,35.22
+0.86,588,294,147,7,3,0.4,3,32.12,34.7
+0.86,588,294,147,7,4,0.4,3,32.05,30.11
+0.86,588,294,147,7,5,0.4,3,31.84,31.6
+0.82,612.5,318.5,147,7,2,0.4,3,28.67,32.43
+0.82,612.5,318.5,147,7,3,0.4,3,29.67,30.65
+0.82,612.5,318.5,147,7,4,0.4,3,29.47,29.77
+0.82,612.5,318.5,147,7,5,0.4,3,28.91,29.64
+0.79,637,343,147,7,2,0.4,3,41.26,46.44
+0.79,637,343,147,7,3,0.4,3,41.3,44.18
+0.79,637,343,147,7,4,0.4,3,42.49,38.81
+0.79,637,343,147,7,5,0.4,3,42.08,38.23
+0.76,661.5,416.5,122.5,7,2,0.4,3,39.32,38.17
+0.76,661.5,416.5,122.5,7,3,0.4,3,39.84,38.48
+0.76,661.5,416.5,122.5,7,4,0.4,3,38.89,39.66
+0.76,661.5,416.5,122.5,7,5,0.4,3,39.68,40.1
+0.74,686,245,220.5,3.5,2,0.4,3,13.97,16.08
+0.74,686,245,220.5,3.5,3,0.4,3,14.22,15.39
+0.74,686,245,220.5,3.5,4,0.4,3,14.1,16.57
+0.74,686,245,220.5,3.5,5,0.4,3,13.78,16.6
+0.71,710.5,269.5,220.5,3.5,2,0.4,3,14.07,16.11
+0.71,710.5,269.5,220.5,3.5,3,0.4,3,14.03,15.47
+0.71,710.5,269.5,220.5,3.5,4,0.4,3,13.94,16.7
+0.71,710.5,269.5,220.5,3.5,5,0.4,3,13.86,16.1
+0.69,735,294,220.5,3.5,2,0.4,3,14.32,16.35
+0.69,735,294,220.5,3.5,3,0.4,3,14.56,15.84
+0.69,735,294,220.5,3.5,4,0.4,3,14.33,16.99
+0.69,735,294,220.5,3.5,5,0.4,3,14.08,17.02
+0.66,759.5,318.5,220.5,3.5,2,0.4,3,15.16,17.04
+0.66,759.5,318.5,220.5,3.5,3,0.4,3,15.18,17.63
+0.66,759.5,318.5,220.5,3.5,4,0.4,3,14.72,18.1
+0.66,759.5,318.5,220.5,3.5,5,0.4,3,14.9,18.22
+0.64,784,343,220.5,3.5,2,0.4,3,18.48,20.78
+0.64,784,343,220.5,3.5,3,0.4,3,18.71,20.72
+0.64,784,343,220.5,3.5,4,0.4,3,18.48,21.54
+0.64,784,343,220.5,3.5,5,0.4,3,18.46,21.53
+0.62,808.5,367.5,220.5,3.5,2,0.4,3,16.47,16.9
+0.62,808.5,367.5,220.5,3.5,3,0.4,3,16.35,17.14
+0.62,808.5,367.5,220.5,3.5,4,0.4,3,16.55,16.56
+0.62,808.5,367.5,220.5,3.5,5,0.4,3,16.74,16
+0.98,514.5,294,110.25,7,2,0.4,4,32.85,32.95
+0.98,514.5,294,110.25,7,3,0.4,4,32.67,33.06
+0.98,514.5,294,110.25,7,4,0.4,4,32.21,33.95
+0.98,514.5,294,110.25,7,5,0.4,4,32.74,33.88
+0.9,563.5,318.5,122.5,7,2,0.4,4,36.45,33.98
+0.9,563.5,318.5,122.5,7,3,0.4,4,35.73,39.92
+0.9,563.5,318.5,122.5,7,4,0.4,4,35.4,39.22
+0.9,563.5,318.5,122.5,7,5,0.4,4,36.57,36.1
+0.86,588,294,147,7,2,0.4,4,32.38,31.53
+0.86,588,294,147,7,3,0.4,4,31.66,36.2
+0.86,588,294,147,7,4,0.4,4,32.15,36.21
+0.86,588,294,147,7,5,0.4,4,32.75,31
+0.82,612.5,318.5,147,7,2,0.4,4,28.93,28.2
+0.82,612.5,318.5,147,7,3,0.4,4,28.05,32.35
+0.82,612.5,318.5,147,7,4,0.4,4,28.64,31.14
+0.82,612.5,318.5,147,7,5,0.4,4,29.52,28.43
+0.79,637,343,147,7,2,0.4,4,42.77,38.33
+0.79,637,343,147,7,3,0.4,4,41.73,47.59
+0.79,637,343,147,7,4,0.4,4,41.32,46.23
+0.79,637,343,147,7,5,0.4,4,42.96,39.56
+0.76,661.5,416.5,122.5,7,2,0.4,4,40.68,40.36
+0.76,661.5,416.5,122.5,7,3,0.4,4,40.4,39.67
+0.76,661.5,416.5,122.5,7,4,0.4,4,40.6,39.85
+0.76,661.5,416.5,122.5,7,5,0.4,4,40.11,40.77
+0.74,686,245,220.5,3.5,2,0.4,4,14.37,16.61
+0.74,686,245,220.5,3.5,3,0.4,4,14.48,16.74
+0.74,686,245,220.5,3.5,4,0.4,4,14.32,16.9
+0.74,686,245,220.5,3.5,5,0.4,4,14.44,17.32
+0.71,710.5,269.5,220.5,3.5,2,0.4,4,14.6,16.85
+0.71,710.5,269.5,220.5,3.5,3,0.4,4,14.7,17.2
+0.71,710.5,269.5,220.5,3.5,4,0.4,4,14.47,17.23
+0.71,710.5,269.5,220.5,3.5,5,0.4,4,14.66,17.74
+0.69,735,294,220.5,3.5,2,0.4,4,14.54,16.81
+0.69,735,294,220.5,3.5,3,0.4,4,14.62,16.88
+0.69,735,294,220.5,3.5,4,0.4,4,14.53,16.9
+0.69,735,294,220.5,3.5,5,0.4,4,14.71,17.39
+0.66,759.5,318.5,220.5,3.5,2,0.4,4,15.34,17.86
+0.66,759.5,318.5,220.5,3.5,3,0.4,4,15.29,17.82
+0.66,759.5,318.5,220.5,3.5,4,0.4,4,15.09,18.36
+0.66,759.5,318.5,220.5,3.5,5,0.4,4,15.3,18.24
+0.64,784,343,220.5,3.5,2,0.4,4,19.06,21.68
+0.64,784,343,220.5,3.5,3,0.4,4,19.13,21.54
+0.64,784,343,220.5,3.5,4,0.4,4,19,22.25
+0.64,784,343,220.5,3.5,5,0.4,4,18.84,22.49
+0.62,808.5,367.5,220.5,3.5,2,0.4,4,16.44,17.1
+0.62,808.5,367.5,220.5,3.5,3,0.4,4,16.9,16.79
+0.62,808.5,367.5,220.5,3.5,4,0.4,4,16.94,16.58
+0.62,808.5,367.5,220.5,3.5,5,0.4,4,16.77,16.79
+0.98,514.5,294,110.25,7,2,0.4,5,32.84,32.88
+0.98,514.5,294,110.25,7,3,0.4,5,32.72,33.23
+0.98,514.5,294,110.25,7,4,0.4,5,32.21,33.76
+0.98,514.5,294,110.25,7,5,0.4,5,32.73,34.01
+0.9,563.5,318.5,122.5,7,2,0.4,5,35.67,33.94
+0.9,563.5,318.5,122.5,7,3,0.4,5,35.01,33.14
+0.9,563.5,318.5,122.5,7,4,0.4,5,34.72,38.79
+0.9,563.5,318.5,122.5,7,5,0.4,5,35.24,37.27
+0.86,588,294,147,7,2,0.4,5,32.31,29.69
+0.86,588,294,147,7,3,0.4,5,31.81,31.2
+0.86,588,294,147,7,4,0.4,5,31.12,36.26
+0.86,588,294,147,7,5,0.4,5,32.06,35.71
+0.82,612.5,318.5,147,7,2,0.4,5,30,29.93
+0.82,612.5,318.5,147,7,3,0.4,5,29.5,29.56
+0.82,612.5,318.5,147,7,4,0.4,5,29.06,33.84
+0.82,612.5,318.5,147,7,5,0.4,5,29.92,32.54
+0.79,637,343,147,7,2,0.4,5,42.11,38.56
+0.79,637,343,147,7,3,0.4,5,41.96,37.7
+0.79,637,343,147,7,4,0.4,5,41.09,47.01
+0.79,637,343,147,7,5,0.4,5,40.79,44.87
+0.76,661.5,416.5,122.5,7,2,0.4,5,38.82,39.37
+0.76,661.5,416.5,122.5,7,3,0.4,5,39.72,39.8
+0.76,661.5,416.5,122.5,7,4,0.4,5,39.31,37.79
+0.76,661.5,416.5,122.5,7,5,0.4,5,39.86,38.18
+0.74,686,245,220.5,3.5,2,0.4,5,14.41,16.69
+0.74,686,245,220.5,3.5,3,0.4,5,14.19,16.62
+0.74,686,245,220.5,3.5,4,0.4,5,14.17,16.94
+0.74,686,245,220.5,3.5,5,0.4,5,14.39,16.7
+0.71,710.5,269.5,220.5,3.5,2,0.4,5,12.43,15.59
+0.71,710.5,269.5,220.5,3.5,3,0.4,5,12.63,14.58
+0.71,710.5,269.5,220.5,3.5,4,0.4,5,12.76,15.33
+0.71,710.5,269.5,220.5,3.5,5,0.4,5,12.42,15.31
+0.69,735,294,220.5,3.5,2,0.4,5,14.12,16.63
+0.69,735,294,220.5,3.5,3,0.4,5,14.28,15.87
+0.69,735,294,220.5,3.5,4,0.4,5,14.37,16.54
+0.69,735,294,220.5,3.5,5,0.4,5,14.21,16.74
+0.66,759.5,318.5,220.5,3.5,2,0.4,5,14.96,17.64
+0.66,759.5,318.5,220.5,3.5,3,0.4,5,14.92,17.79
+0.66,759.5,318.5,220.5,3.5,4,0.4,5,14.92,17.55
+0.66,759.5,318.5,220.5,3.5,5,0.4,5,15.16,18.06
+0.64,784,343,220.5,3.5,2,0.4,5,17.69,20.82
+0.64,784,343,220.5,3.5,3,0.4,5,18.19,20.21
+0.64,784,343,220.5,3.5,4,0.4,5,18.16,20.71
+0.64,784,343,220.5,3.5,5,0.4,5,17.88,21.4
+0.62,808.5,367.5,220.5,3.5,2,0.4,5,16.54,16.88
+0.62,808.5,367.5,220.5,3.5,3,0.4,5,16.44,17.11
+0.62,808.5,367.5,220.5,3.5,4,0.4,5,16.48,16.61
+0.62,808.5,367.5,220.5,3.5,5,0.4,5,16.64,16.03

datasets/forestfires.csv

@@ -0,0 +1,518 @@
+X,Y,month,day,FFMC,DMC,DC,ISI,temp,RH,wind,rain,area
+7,5,mar,fri,86.2,26.2,94.3,5.1,8.2,51,6.7,0,0
+7,4,oct,tue,90.6,35.4,669.1,6.7,18,33,0.9,0,0
+7,4,oct,sat,90.6,43.7,686.9,6.7,14.6,33,1.3,0,0
+8,6,mar,fri,91.7,33.3,77.5,9,8.3,97,4,0.2,0
+8,6,mar,sun,89.3,51.3,102.2,9.6,11.4,99,1.8,0,0
+8,6,aug,sun,92.3,85.3,488,14.7,22.2,29,5.4,0,0
+8,6,aug,mon,92.3,88.9,495.6,8.5,24.1,27,3.1,0,0
+8,6,aug,mon,91.5,145.4,608.2,10.7,8,86,2.2,0,0
+8,6,sep,tue,91,129.5,692.6,7,13.1,63,5.4,0,0
+7,5,sep,sat,92.5,88,698.6,7.1,22.8,40,4,0,0
+7,5,sep,sat,92.5,88,698.6,7.1,17.8,51,7.2,0,0
+7,5,sep,sat,92.8,73.2,713,22.6,19.3,38,4,0,0
+6,5,aug,fri,63.5,70.8,665.3,0.8,17,72,6.7,0,0
+6,5,sep,mon,90.9,126.5,686.5,7,21.3,42,2.2,0,0
+6,5,sep,wed,92.9,133.3,699.6,9.2,26.4,21,4.5,0,0
+6,5,sep,fri,93.3,141.2,713.9,13.9,22.9,44,5.4,0,0
+5,5,mar,sat,91.7,35.8,80.8,7.8,15.1,27,5.4,0,0
+8,5,oct,mon,84.9,32.8,664.2,3,16.7,47,4.9,0,0
+6,4,mar,wed,89.2,27.9,70.8,6.3,15.9,35,4,0,0
+6,4,apr,sat,86.3,27.4,97.1,5.1,9.3,44,4.5,0,0
+6,4,sep,tue,91,129.5,692.6,7,18.3,40,2.7,0,0
+5,4,sep,mon,91.8,78.5,724.3,9.2,19.1,38,2.7,0,0
+7,4,jun,sun,94.3,96.3,200,56.1,21,44,4.5,0,0
+7,4,aug,sat,90.2,110.9,537.4,6.2,19.5,43,5.8,0,0
+7,4,aug,sat,93.5,139.4,594.2,20.3,23.7,32,5.8,0,0
+7,4,aug,sun,91.4,142.4,601.4,10.6,16.3,60,5.4,0,0
+7,4,sep,fri,92.4,117.9,668,12.2,19,34,5.8,0,0
+7,4,sep,mon,90.9,126.5,686.5,7,19.4,48,1.3,0,0
+6,3,sep,sat,93.4,145.4,721.4,8.1,30.2,24,2.7,0,0
+6,3,sep,sun,93.5,149.3,728.6,8.1,22.8,39,3.6,0,0
+6,3,sep,fri,94.3,85.1,692.3,15.9,25.4,24,3.6,0,0
+6,3,sep,mon,88.6,91.8,709.9,7.1,11.2,78,7.6,0,0
+6,3,sep,fri,88.6,69.7,706.8,5.8,20.6,37,1.8,0,0
+6,3,sep,sun,91.7,75.6,718.3,7.8,17.7,39,3.6,0,0
+6,3,sep,mon,91.8,78.5,724.3,9.2,21.2,32,2.7,0,0
+6,3,sep,tue,90.3,80.7,730.2,6.3,18.2,62,4.5,0,0
+6,3,oct,tue,90.6,35.4,669.1,6.7,21.7,24,4.5,0,0
+7,4,oct,fri,90,41.5,682.6,8.7,11.3,60,5.4,0,0
+7,3,oct,sat,90.6,43.7,686.9,6.7,17.8,27,4,0,0
+4,4,mar,tue,88.1,25.7,67.6,3.8,14.1,43,2.7,0,0
+4,4,jul,tue,79.5,60.6,366.7,1.5,23.3,37,3.1,0,0
+4,4,aug,sat,90.2,96.9,624.2,8.9,18.4,42,6.7,0,0
+4,4,aug,tue,94.8,108.3,647.1,17,16.6,54,5.4,0,0
+4,4,sep,sat,92.5,88,698.6,7.1,19.6,48,2.7,0,0
+4,4,sep,wed,90.1,82.9,735.7,6.2,12.9,74,4.9,0,0
+5,6,sep,wed,94.3,85.1,692.3,15.9,25.9,24,4,0,0
+5,6,sep,mon,90.9,126.5,686.5,7,14.7,70,3.6,0,0
+6,6,jul,mon,94.2,62.3,442.9,11,23,36,3.1,0,0
+4,4,mar,mon,87.2,23.9,64.7,4.1,11.8,35,1.8,0,0
+4,4,mar,mon,87.6,52.2,103.8,5,11,46,5.8,0,0
+4,4,sep,thu,92.9,137,706.4,9.2,20.8,17,1.3,0,0
+4,3,aug,sun,90.2,99.6,631.2,6.3,21.5,34,2.2,0,0
+4,3,aug,wed,92.1,111.2,654.1,9.6,20.4,42,4.9,0,0
+4,3,aug,wed,92.1,111.2,654.1,9.6,20.4,42,4.9,0,0
+4,3,aug,thu,91.7,114.3,661.3,6.3,17.6,45,3.6,0,0
+4,3,sep,thu,92.9,137,706.4,9.2,27.7,24,2.2,0,0
+4,3,sep,tue,90.3,80.7,730.2,6.3,17.8,63,4.9,0,0
+4,3,oct,sun,92.6,46.5,691.8,8.8,13.8,50,2.7,0,0
+2,2,feb,mon,84,9.3,34,2.1,13.9,40,5.4,0,0
+2,2,feb,fri,86.6,13.2,43,5.3,12.3,51,0.9,0,0
+2,2,mar,sun,89.3,51.3,102.2,9.6,11.5,39,5.8,0,0
+2,2,mar,sun,89.3,51.3,102.2,9.6,5.5,59,6.3,0,0
+2,2,aug,thu,93,75.3,466.6,7.7,18.8,35,4.9,0,0
+2,2,aug,sun,90.2,99.6,631.2,6.3,20.8,33,2.7,0,0
+2,2,aug,mon,91.1,103.2,638.8,5.8,23.1,31,3.1,0,0
+2,2,aug,thu,91.7,114.3,661.3,6.3,18.6,44,4.5,0,0
+2,2,sep,fri,92.4,117.9,668,12.2,23,37,4.5,0,0
+2,2,sep,fri,92.4,117.9,668,12.2,19.6,33,5.4,0,0
+2,2,sep,fri,92.4,117.9,668,12.2,19.6,33,6.3,0,0
+4,5,mar,fri,91.7,33.3,77.5,9,17.2,26,4.5,0,0
+4,5,mar,fri,91.2,48.3,97.8,12.5,15.8,27,7.6,0,0
+4,5,sep,fri,94.3,85.1,692.3,15.9,17.7,37,3.6,0,0
+5,4,mar,fri,91.7,33.3,77.5,9,15.6,25,6.3,0,0
+5,4,aug,tue,88.8,147.3,614.5,9,17.3,43,4.5,0,0
+5,4,sep,fri,93.3,141.2,713.9,13.9,27.6,30,1.3,0,0
+9,9,feb,thu,84.2,6.8,26.6,7.7,6.7,79,3.1,0,0
+9,9,feb,fri,86.6,13.2,43,5.3,15.7,43,3.1,0,0
+1,3,mar,mon,87.6,52.2,103.8,5,8.3,72,3.1,0,0
+1,2,aug,fri,90.1,108,529.8,12.5,14.7,66,2.7,0,0
+1,2,aug,tue,91,121.2,561.6,7,21.6,19,6.7,0,0
+1,2,aug,sun,91.4,142.4,601.4,10.6,19.5,39,6.3,0,0
+1,2,aug,sun,90.2,99.6,631.2,6.3,17.9,44,2.2,0,0
+1,2,aug,tue,94.8,108.3,647.1,17,18.6,51,4.5,0,0
+1,2,aug,wed,92.1,111.2,654.1,9.6,16.6,47,0.9,0,0
+1,2,aug,thu,91.7,114.3,661.3,6.3,20.2,45,3.6,0,0
+1,2,sep,thu,92.9,137,706.4,9.2,21.5,15,0.9,0,0
+1,2,sep,thu,92.9,137,706.4,9.2,25.4,27,2.2,0,0
+1,2,sep,thu,92.9,137,706.4,9.2,22.4,34,2.2,0,0
+1,2,sep,sun,93.5,149.3,728.6,8.1,25.3,36,3.6,0,0
+6,5,mar,sat,91.7,35.8,80.8,7.8,17.4,25,4.9,0,0
+6,5,aug,sat,90.2,96.9,624.2,8.9,14.7,59,5.8,0,0
+8,6,mar,fri,91.7,35.8,80.8,7.8,17.4,24,5.4,0,0
+8,6,aug,sun,92.3,85.3,488,14.7,20.8,32,6.3,0,0
+8,6,aug,sun,91.4,142.4,601.4,10.6,18.2,43,4.9,0,0
+8,6,aug,mon,91.1,103.2,638.8,5.8,23.4,22,2.7,0,0
+4,4,sep,sun,89.7,90,704.4,4.8,17.8,64,1.3,0,0
+3,4,feb,sat,83.9,8,30.2,2.6,12.7,48,1.8,0,0
+3,4,mar,sat,69,2.4,15.5,0.7,17.4,24,5.4,0,0
+3,4,aug,sun,91.4,142.4,601.4,10.6,11.6,87,4.5,0,0
+3,4,aug,sun,91.4,142.4,601.4,10.6,19.8,39,5.4,0,0
+3,4,aug,sun,91.4,142.4,601.4,10.6,19.8,39,5.4,0,0
+3,4,aug,tue,88.8,147.3,614.5,9,14.4,66,5.4,0,0
+2,4,aug,tue,94.8,108.3,647.1,17,20.1,40,4,0,0
+2,4,sep,sat,92.5,121.1,674.4,8.6,24.1,29,4.5,0,0
+2,4,jan,sat,82.1,3.7,9.3,2.9,5.3,78,3.1,0,0
+4,5,mar,fri,85.9,19.5,57.3,2.8,12.7,52,6.3,0,0
+4,5,mar,thu,91.4,30.7,74.3,7.5,18.2,29,3.1,0,0
+4,5,aug,sun,90.2,99.6,631.2,6.3,21.4,33,3.1,0,0
+4,5,sep,sat,92.5,88,698.6,7.1,20.3,45,3.1,0,0
+4,5,sep,mon,88.6,91.8,709.9,7.1,17.4,56,5.4,0,0
+4,4,mar,fri,85.9,19.5,57.3,2.8,13.7,43,5.8,0,0
+3,4,mar,fri,91.7,33.3,77.5,9,18.8,18,4.5,0,0
+3,4,sep,sun,89.7,90,704.4,4.8,22.8,39,3.6,0,0
+3,4,sep,mon,91.8,78.5,724.3,9.2,18.9,35,2.7,0,0
+3,4,mar,tue,88.1,25.7,67.6,3.8,15.8,27,7.6,0,0
+3,5,mar,tue,88.1,25.7,67.6,3.8,15.5,27,6.3,0,0
+3,4,mar,sat,91.7,35.8,80.8,7.8,11.6,30,6.3,0,0
+3,4,mar,sat,91.7,35.8,80.8,7.8,15.2,27,4.9,0,0
+3,4,mar,mon,90.1,39.7,86.6,6.2,10.6,30,4,0,0
+3,4,aug,thu,93,75.3,466.6,7.7,19.6,36,3.1,0,0
+3,4,aug,mon,91.5,145.4,608.2,10.7,10.3,74,2.2,0,0
+3,4,aug,mon,91.5,145.4,608.2,10.7,17.1,43,5.4,0,0
+3,4,sep,sun,92.4,124.1,680.7,8.5,22.5,42,5.4,0,0
+3,4,sep,tue,84.4,73.4,671.9,3.2,17.9,45,3.1,0,0
+3,4,sep,fri,94.3,85.1,692.3,15.9,19.8,50,5.4,0,0
+3,4,oct,sun,92.6,46.5,691.8,8.8,20.6,24,5.4,0,0
+3,5,mar,mon,87.6,52.2,103.8,5,9,49,2.2,0,0
+3,5,sep,fri,93.5,149.3,728.6,8.1,17.2,43,3.1,0,0
+3,5,oct,wed,91.4,37.9,673.8,5.2,15.9,46,3.6,0,0
+2,5,oct,sun,92.6,46.5,691.8,8.8,15.4,35,0.9,0,0
+4,6,feb,sat,68.2,21.5,87.2,0.8,15.4,40,2.7,0,0
+4,6,mar,mon,87.2,23.9,64.7,4.1,14,39,3.1,0,0
+4,6,mar,sun,89.3,51.3,102.2,9.6,10.6,46,4.9,0,0
+4,6,sep,thu,93.7,80.9,685.2,17.9,17.6,42,3.1,0,0
+3,5,mar,tue,88.1,25.7,67.6,3.8,14.9,38,2.7,0,0
+3,5,aug,sat,93.5,139.4,594.2,20.3,17.6,52,5.8,0,0
+3,6,sep,sun,92.4,124.1,680.7,8.5,17.2,58,1.3,0,0
+3,6,sep,mon,90.9,126.5,686.5,7,15.6,66,3.1,0,0
+9,9,jul,tue,85.8,48.3,313.4,3.9,18,42,2.7,0,0.36
+1,4,sep,tue,91,129.5,692.6,7,21.7,38,2.2,0,0.43
+2,5,sep,mon,90.9,126.5,686.5,7,21.9,39,1.8,0,0.47
+1,2,aug,wed,95.5,99.9,513.3,13.2,23.3,31,4.5,0,0.55
+8,6,aug,fri,90.1,108,529.8,12.5,21.2,51,8.9,0,0.61
+1,2,jul,sat,90,51.3,296.3,8.7,16.6,53,5.4,0,0.71
+2,5,aug,wed,95.5,99.9,513.3,13.2,23.8,32,5.4,0,0.77
+6,5,aug,thu,95.2,131.7,578.8,10.4,27.4,22,4,0,0.9
+5,4,mar,mon,90.1,39.7,86.6,6.2,13.2,40,5.4,0,0.95
+8,3,sep,tue,84.4,73.4,671.9,3.2,24.2,28,3.6,0,0.96
+2,2,aug,tue,94.8,108.3,647.1,17,17.4,43,6.7,0,1.07
+8,6,sep,thu,93.7,80.9,685.2,17.9,23.7,25,4.5,0,1.12
+6,5,jun,fri,92.5,56.4,433.3,7.1,23.2,39,5.4,0,1.19
+9,9,jul,sun,90.1,68.6,355.2,7.2,24.8,29,2.2,0,1.36
+3,4,jul,sat,90.1,51.2,424.1,6.2,24.6,43,1.8,0,1.43
+5,4,sep,fri,94.3,85.1,692.3,15.9,20.1,47,4.9,0,1.46
+1,5,sep,sat,93.4,145.4,721.4,8.1,29.6,27,2.7,0,1.46
+7,4,aug,sun,94.8,108.3,647.1,17,16.4,47,1.3,0,1.56
+2,4,sep,sat,93.4,145.4,721.4,8.1,28.6,27,2.2,0,1.61
+2,2,aug,wed,92.1,111.2,654.1,9.6,18.4,45,3.6,0,1.63
+2,4,aug,wed,92.1,111.2,654.1,9.6,20.5,35,4,0,1.64
+7,4,sep,fri,92.4,117.9,668,12.2,19,34,5.8,0,1.69
+7,4,mar,mon,90.1,39.7,86.6,6.2,16.1,29,3.1,0,1.75
+6,4,aug,thu,95.2,131.7,578.8,10.4,20.3,41,4,0,1.9
+6,3,mar,sat,90.6,50.1,100.4,7.8,15.2,31,8.5,0,1.94
+8,6,sep,sat,92.5,121.1,674.4,8.6,17.8,56,1.8,0,1.95
+8,5,sep,sun,89.7,90,704.4,4.8,17.8,67,2.2,0,2.01
+6,5,mar,thu,84.9,18.2,55,3,5.3,70,4.5,0,2.14
+6,5,aug,wed,92.1,111.2,654.1,9.6,16.6,47,0.9,0,2.29
+6,5,aug,wed,96,127.1,570.5,16.5,23.4,33,4.5,0,2.51
+6,5,mar,fri,91.2,48.3,97.8,12.5,14.6,26,9.4,0,2.53
+8,6,aug,thu,95.2,131.7,578.8,10.4,20.7,45,2.2,0,2.55
+5,4,sep,wed,92.9,133.3,699.6,9.2,21.9,35,1.8,0,2.57
+8,6,aug,wed,85.6,90.4,609.6,6.6,17.4,50,4,0,2.69
+7,4,aug,sun,91.4,142.4,601.4,10.6,20.1,39,5.4,0,2.74
+4,4,sep,mon,90.9,126.5,686.5,7,17.7,39,2.2,0,3.07
+1,4,aug,sat,90.2,96.9,624.2,8.9,14.2,53,1.8,0,3.5
+1,4,aug,sat,90.2,96.9,624.2,8.9,20.3,39,4.9,0,4.53
+6,5,apr,thu,81.5,9.1,55.2,2.7,5.8,54,5.8,0,4.61
+2,5,aug,sun,90.2,99.6,631.2,6.3,19.2,44,2.7,0,4.69
+2,5,sep,wed,90.1,82.9,735.7,6.2,18.3,45,2.2,0,4.88
+8,6,aug,tue,88.8,147.3,614.5,9,14.4,66,5.4,0,5.23
+1,3,sep,sun,92.4,124.1,680.7,8.5,23.9,32,6.7,0,5.33
+8,6,oct,mon,84.9,32.8,664.2,3,19.1,32,4,0,5.44
+5,4,feb,sun,86.8,15.6,48.3,3.9,12.4,53,2.2,0,6.38
+7,4,oct,mon,91.7,48.5,696.1,11.1,16.8,45,4.5,0,6.83
+8,6,aug,fri,93.9,135.7,586.7,15.1,20.8,34,4.9,0,6.96
+2,5,sep,tue,91,129.5,692.6,7,17.6,46,3.1,0,7.04
+8,6,mar,sun,89.3,51.3,102.2,9.6,11.5,39,5.8,0,7.19
+1,5,sep,mon,90.9,126.5,686.5,7,21,42,2.2,0,7.3
+6,4,mar,sat,90.8,41.9,89.4,7.9,13.3,42,0.9,0,7.4
+7,4,mar,sun,90.7,44,92.4,5.5,11.5,60,4,0,8.24
+6,5,mar,fri,91.2,48.3,97.8,12.5,11.7,33,4,0,8.31
+2,5,aug,thu,95.2,131.7,578.8,10.4,24.2,28,2.7,0,8.68
+2,2,aug,tue,94.8,108.3,647.1,17,24.6,22,4.5,0,8.71
+4,5,sep,wed,92.9,133.3,699.6,9.2,24.3,25,4,0,9.41
+2,2,aug,tue,94.8,108.3,647.1,17,24.6,22,4.5,0,10.01
+2,5,aug,fri,93.9,135.7,586.7,15.1,23.5,36,5.4,0,10.02
+6,5,apr,thu,81.5,9.1,55.2,2.7,5.8,54,5.8,0,10.93
+4,5,sep,thu,92.9,137,706.4,9.2,21.5,15,0.9,0,11.06
+3,4,sep,tue,91,129.5,692.6,7,13.9,59,6.3,0,11.24
+2,4,sep,mon,63.5,70.8,665.3,0.8,22.6,38,3.6,0,11.32
+1,5,sep,tue,91,129.5,692.6,7,21.6,33,2.2,0,11.53
+6,5,mar,sun,90.1,37.6,83.7,7.2,12.4,54,3.6,0,12.1
+7,4,feb,sun,83.9,8.7,32.1,2.1,8.8,68,2.2,0,13.05
+8,6,oct,wed,91.4,37.9,673.8,5.2,20.2,37,2.7,0,13.7
+5,6,mar,sat,90.6,50.1,100.4,7.8,15.1,64,4,0,13.99
+4,5,sep,thu,92.9,137,706.4,9.2,22.1,34,1.8,0,14.57
+2,2,aug,sat,93.5,139.4,594.2,20.3,22.9,31,7.2,0,15.45
+7,5,sep,tue,91,129.5,692.6,7,20.7,37,2.2,0,17.2
+6,5,sep,fri,92.4,117.9,668,12.2,19.6,33,6.3,0,19.23
+8,3,sep,thu,93.7,80.9,685.2,17.9,23.2,26,4.9,0,23.41
+4,4,oct,sat,90.6,43.7,686.9,6.7,18.4,25,3.1,0,24.23
+7,4,aug,sat,93.5,139.4,594.2,20.3,5.1,96,5.8,0,26
+7,4,sep,fri,94.3,85.1,692.3,15.9,20.1,47,4.9,0,26.13
+7,3,mar,mon,87.6,52.2,103.8,5,11,46,5.8,0,27.35
+4,4,mar,sat,91.7,35.8,80.8,7.8,17,27,4.9,0,28.66
+4,4,mar,sat,91.7,35.8,80.8,7.8,17,27,4.9,0,28.66
+4,4,sep,sun,92.4,124.1,680.7,8.5,16.9,60,1.3,0,29.48
+1,3,sep,mon,88.6,91.8,709.9,7.1,12.4,73,6.3,0,30.32
+4,5,sep,wed,92.9,133.3,699.6,9.2,19.4,19,1.3,0,31.72
+6,5,mar,mon,90.1,39.7,86.6,6.2,15.2,27,3.1,0,31.86
+8,6,aug,sun,90.2,99.6,631.2,6.3,16.2,59,3.1,0,32.07
+3,4,sep,fri,93.3,141.2,713.9,13.9,18.6,49,3.6,0,35.88
+4,3,mar,mon,87.6,52.2,103.8,5,11,46,5.8,0,36.85
+2,2,jul,fri,88.3,150.3,309.9,6.8,13.4,79,3.6,0,37.02
+7,4,sep,wed,90.1,82.9,735.7,6.2,15.4,57,4.5,0,37.71
+4,4,sep,sun,93.5,149.3,728.6,8.1,22.9,39,4.9,0,48.55
+7,5,oct,mon,91.7,48.5,696.1,11.1,16.1,44,4,0,49.37
+8,6,aug,sat,92.2,81.8,480.8,11.9,20.1,34,4.5,0,58.3
+4,6,sep,sun,93.5,149.3,728.6,8.1,28.3,26,3.1,0,64.1
+8,6,aug,sat,92.2,81.8,480.8,11.9,16.4,43,4,0,71.3
+4,4,sep,wed,92.9,133.3,699.6,9.2,26.4,21,4.5,0,88.49
+1,5,sep,sun,93.5,149.3,728.6,8.1,27.8,27,3.1,0,95.18
+6,4,sep,tue,91,129.5,692.6,7,18.7,43,2.7,0,103.39
+9,4,sep,tue,84.4,73.4,671.9,3.2,24.3,36,3.1,0,105.66
+4,5,sep,sat,92.5,121.1,674.4,8.6,17.7,25,3.1,0,154.88
+8,6,aug,sun,91.4,142.4,601.4,10.6,19.6,41,5.8,0,196.48
+2,2,sep,sat,92.5,121.1,674.4,8.6,18.2,46,1.8,0,200.94
+1,2,sep,tue,91,129.5,692.6,7,18.8,40,2.2,0,212.88
+6,5,sep,sat,92.5,121.1,674.4,8.6,25.1,27,4,0,1090.84
+7,5,apr,sun,81.9,3,7.9,3.5,13.4,75,1.8,0,0
+6,3,apr,wed,88,17.2,43.5,3.8,15.2,51,2.7,0,0
+4,4,apr,fri,83,23.3,85.3,2.3,16.7,20,3.1,0,0
+2,4,aug,sun,94.2,122.3,589.9,12.9,15.4,66,4,0,10.13
+7,4,aug,sun,91.8,175.1,700.7,13.8,21.9,73,7.6,1,0
+2,4,aug,sun,91.8,175.1,700.7,13.8,22.4,54,7.6,0,2.87
+3,4,aug,sun,91.8,175.1,700.7,13.8,26.8,38,6.3,0,0.76
+5,4,aug,sun,91.8,175.1,700.7,13.8,25.7,39,5.4,0,0.09
+2,4,aug,wed,92.2,91.6,503.6,9.6,20.7,70,2.2,0,0.75
+8,6,aug,wed,93.1,157.3,666.7,13.5,28.7,28,2.7,0,0
+3,4,aug,wed,93.1,157.3,666.7,13.5,21.7,40,0.4,0,2.47
+8,5,aug,wed,93.1,157.3,666.7,13.5,26.8,25,3.1,0,0.68
+8,5,aug,wed,93.1,157.3,666.7,13.5,24,36,3.1,0,0.24
+6,5,aug,wed,93.1,157.3,666.7,13.5,22.1,37,3.6,0,0.21
+7,4,aug,thu,91.9,109.2,565.5,8,21.4,38,2.7,0,1.52
+6,3,aug,thu,91.6,138.1,621.7,6.3,18.9,41,3.1,0,10.34
+2,5,aug,thu,87.5,77,694.8,5,22.3,46,4,0,0
+8,6,aug,sat,94.2,117.2,581.1,11,23.9,41,2.2,0,8.02
+4,3,aug,sat,94.2,117.2,581.1,11,21.4,44,2.7,0,0.68
+3,4,aug,sat,91.8,170.9,692.3,13.7,20.6,59,0.9,0,0
+7,4,aug,sat,91.8,170.9,692.3,13.7,23.7,40,1.8,0,1.38
+2,4,aug,mon,93.6,97.9,542,14.4,28.3,32,4,0,8.85
+3,4,aug,fri,91.6,112.4,573,8.9,11.2,84,7.6,0,3.3
+2,4,aug,fri,91.6,112.4,573,8.9,21.4,42,3.1,0,4.25
+6,3,aug,fri,91.1,141.1,629.1,7.1,19.3,39,3.6,0,1.56
+4,4,aug,fri,94.3,167.6,684.4,13,21.8,53,3.1,0,6.54
+4,4,aug,tue,93.7,102.2,550.3,14.6,22.1,54,7.6,0,0.79
+6,5,aug,tue,94.3,131.7,607.1,22.7,19.4,55,4,0,0.17
+2,2,aug,tue,92.1,152.6,658.2,14.3,23.7,24,3.1,0,0
+3,4,aug,tue,92.1,152.6,658.2,14.3,21,32,3.1,0,0
+4,4,aug,tue,92.1,152.6,658.2,14.3,19.1,53,2.7,0,4.4
+2,2,aug,tue,92.1,152.6,658.2,14.3,21.8,56,3.1,0,0.52
+8,6,aug,tue,92.1,152.6,658.2,14.3,20.1,58,4.5,0,9.27
+2,5,aug,tue,92.1,152.6,658.2,14.3,20.2,47,4,0,3.09
+4,6,dec,sun,84.4,27.2,353.5,6.8,4.8,57,8.5,0,8.98
+8,6,dec,wed,84,27.8,354.6,5.3,5.1,61,8,0,11.19
+4,6,dec,thu,84.6,26.4,352,2,5.1,61,4.9,0,5.38
+4,4,dec,mon,85.4,25.4,349.7,2.6,4.6,21,8.5,0,17.85
+3,4,dec,mon,85.4,25.4,349.7,2.6,4.6,21,8.5,0,10.73
+4,4,dec,mon,85.4,25.4,349.7,2.6,4.6,21,8.5,0,22.03
+4,4,dec,mon,85.4,25.4,349.7,2.6,4.6,21,8.5,0,9.77
+4,6,dec,fri,84.7,26.7,352.6,4.1,2.2,59,4.9,0,9.27
+6,5,dec,tue,85.4,25.4,349.7,2.6,5.1,24,8.5,0,24.77
+6,3,feb,sun,84.9,27.5,353.5,3.4,4.2,51,4,0,0
+3,4,feb,wed,86.9,6.6,18.7,3.2,8.8,35,3.1,0,1.1
+5,4,feb,fri,85.2,4.9,15.8,6.3,7.5,46,8,0,24.24
+2,5,jul,sun,93.9,169.7,411.8,12.3,23.4,40,6.3,0,0
+7,6,jul,wed,91.2,183.1,437.7,12.5,12.6,90,7.6,0.2,0
+7,4,jul,sat,91.6,104.2,474.9,9,22.1,49,2.7,0,0
+7,4,jul,sat,91.6,104.2,474.9,9,24.2,32,1.8,0,0
+7,4,jul,sat,91.6,104.2,474.9,9,24.3,30,1.8,0,0
+2,5,jul,sat,91.6,104.2,474.9,9,18.7,53,1.8,0,0
+9,4,jul,sat,91.6,104.2,474.9,9,25.3,39,0.9,0,8
+4,5,jul,fri,91.6,100.2,466.3,6.3,22.9,40,1.3,0,2.64
+7,6,jul,tue,93.1,180.4,430.8,11,26.9,28,5.4,0,86.45
+8,6,jul,tue,92.3,88.8,440.9,8.5,17.1,67,3.6,0,6.57
+7,5,jun,sun,93.1,180.4,430.8,11,22.2,48,1.3,0,0
+6,4,jun,sun,90.4,89.5,290.8,6.4,14.3,46,1.8,0,0.9
+8,6,jun,sun,90.4,89.5,290.8,6.4,15.4,45,2.2,0,0
+8,6,jun,wed,91.2,147.8,377.2,12.7,19.6,43,4.9,0,0
+6,5,jun,sat,53.4,71,233.8,0.4,10.6,90,2.7,0,0
+6,5,jun,mon,90.4,93.3,298.1,7.5,20.7,25,4.9,0,0
+6,5,jun,mon,90.4,93.3,298.1,7.5,19.1,39,5.4,0,3.52
+3,6,jun,fri,91.1,94.1,232.1,7.1,19.2,38,4.5,0,0
+3,6,jun,fri,91.1,94.1,232.1,7.1,19.2,38,4.5,0,0
+6,5,may,sat,85.1,28,113.8,3.5,11.3,94,4.9,0,0
+1,4,sep,sun,89.6,84.1,714.3,5.7,19,52,2.2,0,0
+7,4,sep,sun,89.6,84.1,714.3,5.7,17.1,53,5.4,0,0.41
+3,4,sep,sun,89.6,84.1,714.3,5.7,23.8,35,3.6,0,5.18
+2,4,sep,sun,92.4,105.8,758.1,9.9,16,45,1.8,0,0
+2,4,sep,sun,92.4,105.8,758.1,9.9,24.9,27,2.2,0,0
+7,4,sep,sun,92.4,105.8,758.1,9.9,25.3,27,2.7,0,0
+6,3,sep,sun,92.4,105.8,758.1,9.9,24.8,28,1.8,0,14.29
+2,4,sep,sun,50.4,46.2,706.6,0.4,12.2,78,6.3,0,0
+6,5,sep,wed,92.6,115.4,777.1,8.8,24.3,27,4.9,0,0
+4,4,sep,wed,92.6,115.4,777.1,8.8,19.7,41,1.8,0,1.58
+3,4,sep,wed,91.2,134.7,817.5,7.2,18.5,30,2.7,0,0
+4,5,sep,thu,92.4,96.2,739.4,8.6,18.6,24,5.8,0,0
+4,4,sep,thu,92.4,96.2,739.4,8.6,19.2,24,4.9,0,3.78
+6,5,sep,thu,92.8,119,783.5,7.5,21.6,27,2.2,0,0
+5,4,sep,thu,92.8,119,783.5,7.5,21.6,28,6.3,0,4.41
+6,3,sep,thu,92.8,119,783.5,7.5,18.9,34,7.2,0,34.36
+1,4,sep,thu,92.8,119,783.5,7.5,16.8,28,4,0,7.21
+6,5,sep,thu,92.8,119,783.5,7.5,16.8,28,4,0,1.01
+3,5,sep,thu,90.7,136.9,822.8,6.8,12.9,39,2.7,0,2.18
+6,5,sep,thu,88.1,53.3,726.9,5.4,13.7,56,1.8,0,4.42
+1,4,sep,sat,92.2,102.3,751.5,8.4,24.2,27,3.1,0,0
+5,4,sep,sat,92.2,102.3,751.5,8.4,24.1,27,3.1,0,0
+6,5,sep,sat,92.2,102.3,751.5,8.4,21.2,32,2.2,0,0
+6,5,sep,sat,92.2,102.3,751.5,8.4,19.7,35,1.8,0,0
+4,3,sep,sat,92.2,102.3,751.5,8.4,23.5,27,4,0,3.33
+3,3,sep,sat,92.2,102.3,751.5,8.4,24.2,27,3.1,0,6.58
+7,4,sep,sat,91.2,124.4,795.3,8.5,21.5,28,4.5,0,15.64
+4,4,sep,sat,91.2,124.4,795.3,8.5,17.1,41,2.2,0,11.22
+1,4,sep,mon,92.1,87.7,721.1,9.5,18.1,54,3.1,0,2.13
+2,3,sep,mon,91.6,108.4,764,6.2,18,51,5.4,0,0
+4,3,sep,mon,91.6,108.4,764,6.2,9.8,86,1.8,0,0
+7,4,sep,mon,91.6,108.4,764,6.2,19.3,44,2.2,0,0
+6,3,sep,mon,91.6,108.4,764,6.2,23,34,2.2,0,56.04
+8,6,sep,mon,91.6,108.4,764,6.2,22.7,35,2.2,0,7.48
+2,4,sep,mon,91.6,108.4,764,6.2,20.4,41,1.8,0,1.47
+2,5,sep,mon,91.6,108.4,764,6.2,19.3,44,2.2,0,3.93
+8,6,sep,mon,91.9,111.7,770.3,6.5,15.7,51,2.2,0,0
+6,3,sep,mon,91.5,130.1,807.1,7.5,20.6,37,1.8,0,0
+8,6,sep,mon,91.5,130.1,807.1,7.5,15.9,51,4.5,0,2.18
+6,3,sep,mon,91.5,130.1,807.1,7.5,12.2,66,4.9,0,6.1
+2,2,sep,mon,91.5,130.1,807.1,7.5,16.8,43,3.1,0,5.83
+1,4,sep,mon,91.5,130.1,807.1,7.5,21.3,35,2.2,0,28.19
+5,4,sep,fri,92.1,99,745.3,9.6,10.1,75,3.6,0,0
+3,4,sep,fri,92.1,99,745.3,9.6,17.4,57,4.5,0,0
+5,4,sep,fri,92.1,99,745.3,9.6,12.8,64,3.6,0,1.64
+5,4,sep,fri,92.1,99,745.3,9.6,10.1,75,3.6,0,3.71
+4,4,sep,fri,92.1,99,745.3,9.6,15.4,53,6.3,0,7.31
+7,4,sep,fri,92.1,99,745.3,9.6,20.6,43,3.6,0,2.03
+7,4,sep,fri,92.1,99,745.3,9.6,19.8,47,2.7,0,1.72
+7,4,sep,fri,92.1,99,745.3,9.6,18.7,50,2.2,0,5.97
+4,4,sep,fri,92.1,99,745.3,9.6,20.8,35,4.9,0,13.06
+4,4,sep,fri,92.1,99,745.3,9.6,20.8,35,4.9,0,1.26
+6,3,sep,fri,92.5,122,789.7,10.2,15.9,55,3.6,0,0
+6,3,sep,fri,92.5,122,789.7,10.2,19.7,39,2.7,0,0
+1,4,sep,fri,92.5,122,789.7,10.2,21.1,39,2.2,0,8.12
+6,5,sep,fri,92.5,122,789.7,10.2,18.4,42,2.2,0,1.09
+4,3,sep,fri,92.5,122,789.7,10.2,17.3,45,4,0,3.94
+7,4,sep,fri,88.2,55.2,732.3,11.6,15.2,64,3.1,0,0.52
+4,3,sep,tue,91.9,111.7,770.3,6.5,15.9,53,2.2,0,2.93
+6,5,sep,tue,91.9,111.7,770.3,6.5,21.1,35,2.7,0,5.65
+6,5,sep,tue,91.9,111.7,770.3,6.5,19.6,45,3.1,0,20.03
+4,5,sep,tue,91.1,132.3,812.1,12.5,15.9,38,5.4,0,1.75
+4,5,sep,tue,91.1,132.3,812.1,12.5,16.4,27,3.6,0,0
+6,5,sep,sat,91.2,94.3,744.4,8.4,16.8,47,4.9,0,12.64
+4,5,sep,sun,91,276.3,825.1,7.1,13.8,77,7.6,0,0
+7,4,sep,sun,91,276.3,825.1,7.1,13.8,77,7.6,0,11.06
+3,4,jul,wed,91.9,133.6,520.5,8,14.2,58,4,0,0
+4,5,aug,sun,92,203.2,664.5,8.1,10.4,75,0.9,0,0
+5,4,aug,thu,94.8,222.4,698.6,13.9,20.3,42,2.7,0,0
+6,5,sep,fri,90.3,290,855.3,7.4,10.3,78,4,0,18.3
+6,5,sep,sat,91.2,94.3,744.4,8.4,15.4,57,4.9,0,39.35
+8,6,aug,mon,92.1,207,672.6,8.2,21.1,54,2.2,0,0
+2,2,aug,sat,93.7,231.1,715.1,8.4,21.9,42,2.2,0,174.63
+6,5,mar,thu,90.9,18.9,30.6,8,8.7,51,5.8,0,0
+4,5,jan,sun,18.7,1.1,171.4,0,5.2,100,0.9,0,0
+5,4,jul,wed,93.7,101.3,458.8,11.9,19.3,39,7.2,0,7.73
+8,6,aug,thu,90.7,194.1,643,6.8,16.2,63,2.7,0,16.33
+8,6,aug,wed,95.2,217.7,690,18,28.2,29,1.8,0,5.86
+9,6,aug,thu,91.6,248.4,753.8,6.3,20.5,58,2.7,0,42.87
+8,4,aug,sat,91.6,273.8,819.1,7.7,21.3,44,4.5,0,12.18
+2,4,aug,sun,91.6,181.3,613,7.6,20.9,50,2.2,0,16
+3,4,sep,sun,90.5,96.7,750.5,11.4,20.6,55,5.4,0,24.59
+5,5,mar,thu,90.9,18.9,30.6,8,11.6,48,5.4,0,0
+6,4,aug,fri,94.8,227,706.7,12,23.3,34,3.1,0,28.74
+7,4,aug,fri,94.8,227,706.7,12,23.3,34,3.1,0,0
+7,4,feb,mon,84.7,9.5,58.3,4.1,7.5,71,6.3,0,9.96
+8,6,sep,fri,91.1,91.3,738.1,7.2,20.7,46,2.7,0,30.18
+1,3,sep,sun,91,276.3,825.1,7.1,21.9,43,4,0,70.76
+2,4,mar,tue,93.4,15,25.6,11.4,15.2,19,7.6,0,0
+6,5,feb,mon,84.1,4.6,46.7,2.2,5.3,68,1.8,0,0
+4,5,feb,sun,85,9,56.9,3.5,10.1,62,1.8,0,51.78
+4,3,sep,sun,90.5,96.7,750.5,11.4,20.4,55,4.9,0,3.64
+5,6,aug,sun,91.6,181.3,613,7.6,24.3,33,3.6,0,3.63
+1,2,aug,sat,93.7,231.1,715.1,8.4,25.9,32,3.1,0,0
+9,5,jun,wed,93.3,49.5,297.7,14,28,34,4.5,0,0
+9,5,jun,wed,93.3,49.5,297.7,14,28,34,4.5,0,8.16
+3,4,sep,thu,91.1,88.2,731.7,8.3,22.8,46,4,0,4.95
+9,9,aug,fri,94.8,227,706.7,12,25,36,4,0,0
+8,6,aug,thu,90.7,194.1,643,6.8,21.3,41,3.6,0,0
+2,4,sep,wed,87.9,84.8,725.1,3.7,21.8,34,2.2,0,6.04
+2,2,aug,tue,94.6,212.1,680.9,9.5,27.9,27,2.2,0,0
+6,5,sep,sat,87.1,291.3,860.6,4,17,67,4.9,0,3.95
+4,5,feb,sat,84.7,8.2,55,2.9,14.2,46,4,0,0
+4,3,sep,fri,90.3,290,855.3,7.4,19.9,44,3.1,0,7.8
+1,4,jul,tue,92.3,96.2,450.2,12.1,23.4,31,5.4,0,0
+6,3,feb,fri,84.1,7.3,52.8,2.7,14.7,42,2.7,0,0
+7,4,feb,fri,84.6,3.2,43.6,3.3,8.2,53,9.4,0,4.62
+9,4,jul,mon,92.3,92.1,442.1,9.8,22.8,27,4.5,0,1.63
+7,5,aug,sat,93.7,231.1,715.1,8.4,26.4,33,3.6,0,0
+5,4,aug,sun,93.6,235.1,723.1,10.1,24.1,50,4,0,0
+8,6,aug,thu,94.8,222.4,698.6,13.9,27.5,27,4.9,0,746.28
+6,3,jul,tue,92.7,164.1,575.8,8.9,26.3,39,3.1,0,7.02
+6,5,mar,wed,93.4,17.3,28.3,9.9,13.8,24,5.8,0,0
+2,4,aug,sun,92,203.2,664.5,8.1,24.9,42,5.4,0,2.44
+2,5,aug,sun,91.6,181.3,613,7.6,24.8,36,4,0,3.05
+8,8,aug,wed,91.7,191.4,635.9,7.8,26.2,36,4.5,0,185.76
+2,4,aug,wed,95.2,217.7,690,18,30.8,19,4.5,0,0
+8,6,jul,sun,88.9,263.1,795.9,5.2,29.3,27,3.6,0,6.3
+1,3,sep,sat,91.2,94.3,744.4,8.4,22.3,48,4,0,0.72
+8,6,aug,sat,93.7,231.1,715.1,8.4,26.9,31,3.6,0,4.96
+2,2,aug,thu,91.6,248.4,753.8,6.3,20.4,56,2.2,0,0
+8,6,aug,thu,91.6,248.4,753.8,6.3,20.4,56,2.2,0,0
+2,4,aug,mon,92.1,207,672.6,8.2,27.9,33,2.2,0,2.35
+1,3,aug,thu,94.8,222.4,698.6,13.9,26.2,34,5.8,0,0
+3,4,aug,sun,91.6,181.3,613,7.6,24.6,44,4,0,3.2
+7,4,sep,thu,89.7,287.2,849.3,6.8,19.4,45,3.6,0,0
+1,3,aug,sat,92.1,178,605.3,9.6,23.3,40,4,0,6.36
+8,6,aug,thu,94.8,222.4,698.6,13.9,23.9,38,6.7,0,0
+2,4,aug,sun,93.6,235.1,723.1,10.1,20.9,66,4.9,0,15.34
+1,4,aug,fri,90.6,269.8,811.2,5.5,22.2,45,3.6,0,0
+2,5,jul,sat,90.8,84.7,376.6,5.6,23.8,51,1.8,0,0
+8,6,aug,mon,92.1,207,672.6,8.2,26.8,35,1.3,0,0.54
+8,6,aug,sat,89.4,253.6,768.4,9.7,14.2,73,2.7,0,0
+2,5,aug,sat,93.7,231.1,715.1,8.4,23.6,53,4,0,6.43
+1,3,sep,fri,91.1,91.3,738.1,7.2,19.1,46,2.2,0,0.33
+5,4,sep,fri,90.3,290,855.3,7.4,16.2,58,3.6,0,0
+8,6,aug,mon,92.1,207,672.6,8.2,25.5,29,1.8,0,1.23
+6,5,apr,mon,87.9,24.9,41.6,3.7,10.9,64,3.1,0,3.35
+1,2,jul,fri,90.7,80.9,368.3,16.8,14.8,78,8,0,0
+2,5,sep,fri,90.3,290,855.3,7.4,16.2,58,3.6,0,9.96
+5,5,aug,sun,94,47.9,100.7,10.7,17.3,80,4.5,0,0
+6,5,aug,sun,92,203.2,664.5,8.1,19.1,70,2.2,0,0
+3,4,mar,wed,93.4,17.3,28.3,9.9,8.9,35,8,0,0
+7,4,sep,wed,89.7,284.9,844,10.1,10.5,77,4,0,0
+7,4,aug,sun,91.6,181.3,613,7.6,19.3,61,4.9,0,0
+4,5,aug,wed,95.2,217.7,690,18,23.4,49,5.4,0,6.43
+1,4,aug,fri,90.5,196.8,649.9,16.3,11.8,88,4.9,0,9.71
+7,4,aug,mon,91.5,238.2,730.6,7.5,17.7,65,4,0,0
+4,5,aug,thu,89.4,266.2,803.3,5.6,17.4,54,3.1,0,0
+3,4,aug,thu,91.6,248.4,753.8,6.3,16.8,56,3.1,0,0
+3,4,jul,mon,94.6,160,567.2,16.7,17.9,48,2.7,0,0
+2,4,aug,thu,91.6,248.4,753.8,6.3,16.6,59,2.7,0,0
+1,4,aug,wed,91.7,191.4,635.9,7.8,19.9,50,4,0,82.75
+8,6,aug,sat,93.7,231.1,715.1,8.4,18.9,64,4.9,0,3.32
+7,4,aug,sat,91.6,273.8,819.1,7.7,15.5,72,8,0,1.94
+2,5,aug,sat,93.7,231.1,715.1,8.4,18.9,64,4.9,0,0
+8,6,aug,sat,93.7,231.1,715.1,8.4,18.9,64,4.9,0,0
+1,4,sep,sun,91,276.3,825.1,7.1,14.5,76,7.6,0,3.71
+6,5,feb,tue,75.1,4.4,16.2,1.9,4.6,82,6.3,0,5.39
+6,4,feb,tue,75.1,4.4,16.2,1.9,5.1,77,5.4,0,2.14
+2,2,feb,sat,79.5,3.6,15.3,1.8,4.6,59,0.9,0,6.84
+6,5,mar,mon,87.2,15.1,36.9,7.1,10.2,45,5.8,0,3.18
+3,4,mar,wed,90.2,18.5,41.1,7.3,11.2,41,5.4,0,5.55
+6,5,mar,thu,91.3,20.6,43.5,8.5,13.3,27,3.6,0,6.61
+6,3,apr,sun,91,14.6,25.6,12.3,13.7,33,9.4,0,61.13
+5,4,apr,sun,91,14.6,25.6,12.3,17.6,27,5.8,0,0
+4,3,may,fri,89.6,25.4,73.7,5.7,18,40,4,0,38.48
+8,3,jun,mon,88.2,96.2,229,4.7,14.3,79,4,0,1.94
+9,4,jun,sat,90.5,61.1,252.6,9.4,24.5,50,3.1,0,70.32
+4,3,jun,thu,93,103.8,316.7,10.8,26.4,35,2.7,0,10.08
+2,5,jun,thu,93.7,121.7,350.2,18,22.7,40,9.4,0,3.19
+4,3,jul,thu,93.5,85.3,395,9.9,27.2,28,1.3,0,1.76
+4,3,jul,sun,93.7,101.3,423.4,14.7,26.1,45,4,0,7.36
+7,4,jul,sun,93.7,101.3,423.4,14.7,18.2,82,4.5,0,2.21
+7,4,jul,mon,89.2,103.9,431.6,6.4,22.6,57,4.9,0,278.53
+9,9,jul,thu,93.2,114.4,560,9.5,30.2,25,4.5,0,2.75
+4,3,jul,thu,93.2,114.4,560,9.5,30.2,22,4.9,0,0
+3,4,aug,sun,94.9,130.3,587.1,14.1,23.4,40,5.8,0,1.29
+8,6,aug,sun,94.9,130.3,587.1,14.1,31,27,5.4,0,0
+2,5,aug,sun,94.9,130.3,587.1,14.1,33.1,25,4,0,26.43
+2,4,aug,mon,95,135.5,596.3,21.3,30.6,28,3.6,0,2.07
+5,4,aug,tue,95.1,141.3,605.8,17.7,24.1,43,6.3,0,2
+5,4,aug,tue,95.1,141.3,605.8,17.7,26.4,34,3.6,0,16.4
+4,4,aug,tue,95.1,141.3,605.8,17.7,19.4,71,7.6,0,46.7
+4,4,aug,wed,95.1,141.3,605.8,17.7,20.6,58,1.3,0,0
+4,4,aug,wed,95.1,141.3,605.8,17.7,28.7,33,4,0,0
+4,4,aug,thu,95.8,152,624.1,13.8,32.4,21,4.5,0,0
+1,3,aug,fri,95.9,158,633.6,11.3,32.4,27,2.2,0,0
+1,3,aug,fri,95.9,158,633.6,11.3,27.5,29,4.5,0,43.32
+6,6,aug,sat,96,164,643,14,30.8,30,4.9,0,8.59
+6,6,aug,mon,96.2,175.5,661.8,16.8,23.9,42,2.2,0,0
+4,5,aug,mon,96.2,175.5,661.8,16.8,32.6,26,3.1,0,2.77
+3,4,aug,tue,96.1,181.1,671.2,14.3,32.3,27,2.2,0,14.68
+6,5,aug,tue,96.1,181.1,671.2,14.3,33.3,26,2.7,0,40.54
+7,5,aug,tue,96.1,181.1,671.2,14.3,27.3,63,4.9,6.4,10.82
+8,6,aug,tue,96.1,181.1,671.2,14.3,21.6,65,4.9,0.8,0
+7,5,aug,tue,96.1,181.1,671.2,14.3,21.6,65,4.9,0.8,0
+4,4,aug,tue,96.1,181.1,671.2,14.3,20.7,69,4.9,0.4,0
+2,4,aug,wed,94.5,139.4,689.1,20,29.2,30,4.9,0,1.95
+4,3,aug,wed,94.5,139.4,689.1,20,28.9,29,4.9,0,49.59
+1,2,aug,thu,91,163.2,744.4,10.1,26.7,35,1.8,0,5.8
+1,2,aug,fri,91,166.9,752.6,7.1,18.5,73,8.5,0,0
+2,4,aug,fri,91,166.9,752.6,7.1,25.9,41,3.6,0,0
+1,2,aug,fri,91,166.9,752.6,7.1,25.9,41,3.6,0,0
+5,4,aug,fri,91,166.9,752.6,7.1,21.1,71,7.6,1.4,2.17
+6,5,aug,fri,91,166.9,752.6,7.1,18.2,62,5.4,0,0.43
+8,6,aug,sun,81.6,56.7,665.6,1.9,27.8,35,2.7,0,0
+4,3,aug,sun,81.6,56.7,665.6,1.9,27.8,32,2.7,0,6.44
+2,4,aug,sun,81.6,56.7,665.6,1.9,21.9,71,5.8,0,54.29
+7,4,aug,sun,81.6,56.7,665.6,1.9,21.2,70,6.7,0,11.16
+1,4,aug,sat,94.4,146,614.7,11.3,25.6,42,4,0,0
+6,3,nov,tue,79.5,3,106.7,1.1,11.8,31,4.5,0,0

datasets/slump_test.data

@@ -0,0 +1,104 @@
+No,Cement,Slag,Fly ash,Water,SP,Coarse Aggr.,Fine Aggr.,SLUMP(cm),FLOW(cm),Compressive Strength (28-day)(Mpa)
+1,273,82,105,210,9,904,680,23,62,34.99
+2,163,149,191,180,12,843,746,0,20,41.14
+3,162,148,191,179,16,840,743,1,20,41.81
+4,162,148,190,179,19,838,741,3,21.5,42.08
+5,154,112,144,220,10,923,658,20,64,26.82
+6,147,89,115,202,9,860,829,23,55,25.21
+7,152,139,178,168,18,944,695,0,20,38.86
+8,145,0,227,240,6,750,853,14.5,58.5,36.59
+9,152,0,237,204,6,785,892,15.5,51,32.71
+10,304,0,140,214,6,895,722,19,51,38.46
+11,145,106,136,208,10,751,883,24.5,61,26.02
+12,148,109,139,193,7,768,902,23.75,58,28.03
+13,142,130,167,215,6,735,836,25.5,67,31.37
+14,354,0,0,234,6,959,691,17,54,33.91
+15,374,0,0,190,7,1013,730,14.5,42.5,32.44
+16,159,116,149,175,15,953,720,23.5,54.5,34.05
+17,153,0,239,200,6,1002,684,12,35,28.29
+18,295,106,136,206,11,750,766,25,68.5,41.01
+19,310,0,143,168,10,914,804,20.5,48.2,49.3
+20,296,97,0,219,9,932,685,15,48.5,29.23
+21,305,100,0,196,10,959,705,20,49,29.77
+22,310,0,143,218,10,787,804,13,46,36.19
+23,148,180,0,183,11,972,757,0,20,18.52
+24,146,178,0,192,11,961,749,18,46,17.19
+25,142,130,167,174,11,883,785,0,20,36.72
+26,140,128,164,183,12,871,775,23.75,53,33.38
+27,308,111,142,217,10,783,686,25,70,42.08
+28,295,106,136,208,6,871,650,26.5,70,39.4
+29,298,107,137,201,6,878,655,16,26,41.27
+30,314,0,161,207,6,851,757,21.5,64,41.14
+31,321,0,164,190,5,870,774,24,60,45.82
+32,349,0,178,230,6,785,721,20,68.5,43.95
+33,366,0,187,191,7,824,757,24.75,62.7,52.65
+34,274,89,115,202,9,759,827,26.5,68,35.52
+35,137,167,214,226,6,708,757,27.5,70,34.45
+36,275,99,127,184,13,810,790,25.75,64.5,43.54
+37,252,76,97,194,8,835,821,23,54,33.11
+38,165,150,0,182,12,1023,729,14.5,20,18.26
+39,158,0,246,174,7,1035,706,19,43,34.99
+40,156,0,243,180,11,1022,698,21,57,33.78
+41,145,177,227,209,11,752,715,2.5,20,35.66
+42,154,141,181,234,11,797,683,23,65,33.51
+43,160,146,188,203,11,829,710,13,38,33.51
+44,291,105,0,205,6,859,797,24,59,27.62
+45,298,107,0,186,6,879,815,3,20,30.97
+46,318,126,0,210,6,861,737,17.5,48,31.77
+47,280,92,118,207,9,883,679,25.5,64,37.39
+48,287,94,121,188,9,904,696,25,61,43.01
+49,332,0,170,160,6,900,806,0,20,58.53
+50,326,0,167,174,6,884,792,21.5,42,52.65
+51,320,0,163,188,9,866,776,23.5,60,45.69
+52,342,136,0,225,11,770,747,21,61,32.04
+53,356,142,0,193,11,801,778,8,30,36.46
+54,309,0,142,218,10,912,680,24,62,38.59
+55,322,0,149,186,8,951,709,20.5,61.5,45.42
+56,159,193,0,208,12,821,818,23,50,19.19
+57,307,110,0,189,10,904,765,22,40,31.5
+58,313,124,0,205,11,846,758,22,49,29.63
+59,143,131,168,217,6,891,672,25,69,26.42
+60,140,128,164,237,6,869,656,24,65,29.5
+61,278,0,117,205,9,875,799,19,48,32.71
+62,288,0,121,177,7,908,829,22.5,48.5,39.93
+63,299,107,0,210,10,881,745,25,63,28.29
+64,291,104,0,231,9,857,725,23,69,30.43
+65,265,86,111,195,6,833,790,27,60,37.39
+66,159,0,248,175,12,1041,683,21,51,35.39
+67,160,0,250,168,12,1049,688,18,48,37.66
+68,166,0,260,183,13,859,827,21,54,40.34
+69,320,127,164,211,6,721,723,2,20,46.36
+70,336,134,0,222,6,756,787,26,64,31.9
+71,276,90,116,180,9,870,768,0,20,44.08
+72,313,112,0,220,10,794,789,23,58,28.16
+73,322,116,0,196,10,818,813,25.5,67,29.77
+74,294,106,136,207,6,747,778,24,47,41.27
+75,146,106,137,209,6,875,765,24,67,27.89
+76,149,109,139,193,6,892,780,23.5,58.5,28.7
+77,159,0,187,176,11,990,789,12,39,32.57
+78,261,78,100,201,9,864,761,23,63.5,34.18
+79,140,1.4,198.1,174.9,4.4,1049.9,780.5,16.25,31,30.83
+80,141.1,0.6,209.5,188.8,4.6,996.1,789.2,23.5,53,30.43
+81,140.1,4.2,215.9,193.9,4.7,1049.5,710.1,24.5,57,26.42
+82,140.1,11.8,226.1,207.8,4.9,1020.9,683.8,21,64,26.28
+83,160.2,0.3,240,233.5,9.2,781,841.1,24,75,36.19
+84,140.2,30.5,239,169.4,5.3,1028.4,742.7,21.25,46,36.32
+85,140.2,44.8,234.9,171.3,5.5,1047.6,704,23.5,52.5,33.78
+86,140.5,61.1,238.9,182.5,5.7,1017.7,681.4,24.5,60,30.97
+87,143.3,91.8,239.8,200.8,6.2,964.8,647.1,25,55,27.09
+88,194.3,0.3,240,234.2,8.9,780.6,811.3,26.5,78,38.46
+89,150.4,110.9,239.7,168.1,6.5,1000.2,667.2,9.5,27.5,37.92
+90,150.3,111.4,238.8,167.3,6.5,999.5,670.5,14.5,36.5,38.19
+91,155.4,122.1,240,179.9,6.7,966.8,652.5,14.5,41.5,35.52
+92,165.3,143.2,238.3,200.4,7.1,883.2,652.6,17,27,32.84
+93,303.8,0.2,239.8,236.4,8.3,780.1,715.3,25,78,44.48
+94,172,162.1,238.5,166,7.4,953.3,641.4,0,20,41.54
+95,172.8,158.3,239.5,166.4,7.4,952.6,644.1,0,20,41.81
+96,184.3,153.4,239.2,179,7.5,920.2,640.9,0,20,41.01
+97,215.6,112.9,239,198.7,7.4,884,649.1,27.5,64,39.13
+98,295.3,0,239.9,236.2,8.3,780.3,722.9,25,77,44.08
+99,248.3,101,239.1,168.9,7.7,954.2,640.6,0,20,49.97
+100,248,101,239.9,169.1,7.7,949.9,644.1,2,20,50.23
+101,258.8,88,239.6,175.3,7.6,938.9,646,0,20,50.5
+102,297.1,40.9,239.9,194,7.5,908.9,651.8,27.5,67,49.17
+103,348.7,0.1,223.1,208.5,9.6,786.2,758.1,29,78,48.77

datasets/traffic_flow_forecasting/traffic_dataset.mat

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:45a3f8b443fd48e2d687d3169d95ac68b6bc31999343b1885eadb787d3aaedbc
+size 4401723

datasets/yacht_hydrodynamics.data

@@ -0,0 +1,309 @@
+-2.3 0.568 4.78 3.99 3.17 0.125 0.11
+-2.3 0.568 4.78 3.99 3.17 0.150 0.27
+-2.3 0.568 4.78 3.99 3.17 0.175 0.47
+-2.3 0.568 4.78 3.99 3.17 0.200 0.78
+-2.3 0.568 4.78 3.99 3.17 0.225 1.18
+-2.3 0.568 4.78 3.99 3.17 0.250 1.82
+-2.3 0.568 4.78 3.99 3.17 0.275 2.61
+-2.3 0.568 4.78 3.99 3.17 0.300 3.76
+-2.3 0.568 4.78 3.99 3.17 0.325 4.99
+-2.3 0.568 4.78 3.99 3.17 0.350 7.16
+-2.3 0.568 4.78 3.99 3.17 0.375 11.93
+-2.3 0.568 4.78 3.99 3.17 0.400 20.11
+-2.3 0.568 4.78 3.99 3.17 0.425 32.75
+-2.3 0.568 4.78 3.99 3.17 0.450 49.49
+-2.3 0.569 4.78 3.04 3.64 0.125 0.04
+-2.3 0.569 4.78 3.04 3.64 0.150 0.17
+-2.3 0.569 4.78 3.04 3.64 0.175 0.37
+-2.3 0.569 4.78 3.04 3.64 0.200 0.66
+-2.3 0.569 4.78 3.04 3.64 0.225 1.06
+-2.3 0.569 4.78 3.04 3.64 0.250 1.59
+-2.3 0.569 4.78 3.04 3.64 0.275 2.33
+-2.3 0.569 4.78 3.04 3.64 0.300 3.29
+-2.3 0.569 4.78 3.04 3.64 0.325 4.61
+-2.3 0.569 4.78 3.04 3.64 0.350 7.11
+-2.3 0.569 4.78 3.04 3.64 0.375 11.99
+-2.3 0.569 4.78 3.04 3.64 0.400 21.09
+-2.3 0.569 4.78 3.04 3.64 0.425 35.01
+-2.3 0.569 4.78 3.04 3.64 0.450 51.80
+-2.3 0.565 4.78 5.35 2.76 0.125 0.09
+-2.3 0.565 4.78 5.35 2.76 0.150 0.29
+-2.3 0.565 4.78 5.35 2.76 0.175 0.56
+-2.3 0.565 4.78 5.35 2.76 0.200 0.86
+-2.3 0.565 4.78 5.35 2.76 0.225 1.31
+-2.3 0.565 4.78 5.35 2.76 0.250 1.99
+-2.3 0.565 4.78 5.35 2.76 0.275 2.94
+-2.3 0.565 4.78 5.35 2.76 0.300 4.21
+-2.3 0.565 4.78 5.35 2.76 0.325 5.54
+-2.3 0.565 4.78 5.35 2.76 0.350 8.25
+-2.3 0.565 4.78 5.35 2.76 0.375 13.08
+-2.3 0.565 4.78 5.35 2.76 0.400 21.40
+-2.3 0.565 4.78 5.35 2.76 0.425 33.14
+-2.3 0.565 4.78 5.35 2.76 0.450 50.14
+-2.3 0.564 5.10 3.95 3.53 0.125 0.20
+-2.3 0.564 5.10 3.95 3.53 0.150 0.35
+-2.3 0.564 5.10 3.95 3.53 0.175 0.65
+-2.3 0.564 5.10 3.95 3.53 0.200 0.93
+-2.3 0.564 5.10 3.95 3.53 0.225 1.37
+-2.3 0.564 5.10 3.95 3.53 0.250 1.97
+-2.3 0.564 5.10 3.95 3.53 0.275 2.83
+-2.3 0.564 5.10 3.95 3.53 0.300 3.99
+-2.3 0.564 5.10 3.95 3.53 0.325 5.19
+-2.3 0.564 5.10 3.95 3.53 0.350 8.03
+-2.3 0.564 5.10 3.95 3.53 0.375 12.86
+-2.3 0.564 5.10 3.95 3.53 0.400 21.51
+-2.3 0.564 5.10 3.95 3.53 0.425 33.97
+-2.3 0.564 5.10 3.95 3.53 0.450 50.36
+-2.4 0.574 4.36 3.96 2.76 0.125 0.20
+-2.4 0.574 4.36 3.96 2.76 0.150 0.35
+-2.4 0.574 4.36 3.96 2.76 0.175 0.65
+-2.4 0.574 4.36 3.96 2.76 0.200 0.93
+-2.4 0.574 4.36 3.96 2.76 0.225 1.37
+-2.4 0.574 4.36 3.96 2.76 0.250 1.97
+-2.4 0.574 4.36 3.96 2.76 0.275 2.83
+-2.4 0.574 4.36 3.96 2.76 0.300 3.99
+-2.4 0.574 4.36 3.96 2.76 0.325 5.19
+-2.4 0.574 4.36 3.96 2.76 0.350 8.03
+-2.4 0.574 4.36 3.96 2.76 0.375 12.86
+-2.4 0.574 4.36 3.96 2.76 0.400 21.51
+-2.4 0.574 4.36 3.96 2.76 0.425 33.97
+-2.4 0.574 4.36 3.96 2.76 0.450 50.36
+-2.4 0.568 4.34 2.98 3.15 0.125 0.12
+-2.4 0.568 4.34 2.98 3.15 0.150 0.26
+-2.4 0.568 4.34 2.98 3.15 0.175 0.43
+-2.4 0.568 4.34 2.98 3.15 0.200 0.69
+-2.4 0.568 4.34 2.98 3.15 0.225 1.09
+-2.4 0.568 4.34 2.98 3.15 0.250 1.67
+-2.4 0.568 4.34 2.98 3.15 0.275 2.46
+-2.4 0.568 4.34 2.98 3.15 0.300 3.43
+-2.4 0.568 4.34 2.98 3.15 0.325 4.62
+-2.4 0.568 4.34 2.98 3.15 0.350 6.86
+-2.4 0.568 4.34 2.98 3.15 0.375 11.56
+-2.4 0.568 4.34 2.98 3.15 0.400 20.63
+-2.4 0.568 4.34 2.98 3.15 0.425 34.50
+-2.4 0.568 4.34 2.98 3.15 0.450 54.23
+-2.3 0.562 5.14 4.95 3.17 0.125 0.28
+-2.3 0.562 5.14 4.95 3.17 0.150 0.44
+-2.3 0.562 5.14 4.95 3.17 0.175 0.70
+-2.3 0.562 5.14 4.95 3.17 0.200 1.07
+-2.3 0.562 5.14 4.95 3.17 0.225 1.57
+-2.3 0.562 5.14 4.95 3.17 0.250 2.23
+-2.3 0.562 5.14 4.95 3.17 0.275 3.09
+-2.3 0.562 5.14 4.95 3.17 0.300 4.09
+-2.3 0.562 5.14 4.95 3.17 0.325 5.82
+-2.3 0.562 5.14 4.95 3.17 0.350 8.28
+-2.3 0.562 5.14 4.95 3.17 0.375 12.80
+-2.3 0.562 5.14 4.95 3.17 0.400 20.41
+-2.3 0.562 5.14 4.95 3.17 0.425 32.34
+-2.3 0.562 5.14 4.95 3.17 0.450 47.29
+-2.4 0.585 4.78 3.84 3.32 0.125 0.20
+-2.4 0.585 4.78 3.84 3.32 0.150 0.38
+-2.4 0.585 4.78 3.84 3.32 0.175 0.64
+-2.4 0.585 4.78 3.84 3.32 0.200 0.97
+-2.4 0.585 4.78 3.84 3.32 0.225 1.36
+-2.4 0.585 4.78 3.84 3.32 0.250 1.98
+-2.4 0.585 4.78 3.84 3.32 0.275 2.91
+-2.4 0.585 4.78 3.84 3.32 0.300 4.35
+-2.4 0.585 4.78 3.84 3.32 0.325 5.79
+-2.4 0.585 4.78 3.84 3.32 0.350 8.04
+-2.4 0.585 4.78 3.84 3.32 0.375 12.15
+-2.4 0.585 4.78 3.84 3.32 0.400 19.18
+-2.4 0.585 4.78 3.84 3.32 0.425 30.09
+-2.4 0.585 4.78 3.84 3.32 0.450 44.38
+-2.2 0.546 4.78 4.13 3.07 0.125 0.15
+-2.2 0.546 4.78 4.13 3.07 0.150 0.32
+-2.2 0.546 4.78 4.13 3.07 0.175 0.55
+-2.2 0.546 4.78 4.13 3.07 0.200 0.86
+-2.2 0.546 4.78 4.13 3.07 0.225 1.24
+-2.2 0.546 4.78 4.13 3.07 0.250 1.76
+-2.2 0.546 4.78 4.13 3.07 0.275 2.49
+-2.2 0.546 4.78 4.13 3.07 0.300 3.45
+-2.2 0.546 4.78 4.13 3.07 0.325 4.83
+-2.2 0.546 4.78 4.13 3.07 0.350 7.37
+-2.2 0.546 4.78 4.13 3.07 0.375 12.76
+-2.2 0.546 4.78 4.13 3.07 0.400 21.99
+-2.2 0.546 4.78 4.13 3.07 0.425 35.64
+-2.2 0.546 4.78 4.13 3.07 0.450 53.07
+0.0 0.565 4.77 3.99 3.15 0.125 0.11
+0.0 0.565 4.77 3.99 3.15 0.150 0.24
+0.0 0.565 4.77 3.99 3.15 0.175 0.49
+0.0 0.565 4.77 3.99 3.15 0.200 0.79
+0.0 0.565 4.77 3.99 3.15 0.225 1.28
+0.0 0.565 4.77 3.99 3.15 0.250 1.96
+0.0 0.565 4.77 3.99 3.15 0.275 2.88
+0.0 0.565 4.77 3.99 3.15 0.300 4.14
+0.0 0.565 4.77 3.99 3.15 0.325 5.96
+0.0 0.565 4.77 3.99 3.15 0.350 9.07
+0.0 0.565 4.77 3.99 3.15 0.375 14.93
+0.0 0.565 4.77 3.99 3.15 0.400 24.13
+0.0 0.565 4.77 3.99 3.15 0.425 38.12
+0.0 0.565 4.77 3.99 3.15 0.450 55.44
+-5.0 0.565 4.77 3.99 3.15 0.125 0.07
+-5.0 0.565 4.77 3.99 3.15 0.150 0.18
+-5.0 0.565 4.77 3.99 3.15 0.175 0.40
+-5.0 0.565 4.77 3.99 3.15 0.200 0.70
+-5.0 0.565 4.77 3.99 3.15 0.225 1.14
+-5.0 0.565 4.77 3.99 3.15 0.250 1.83
+-5.0 0.565 4.77 3.99 3.15 0.275 2.77
+-5.0 0.565 4.77 3.99 3.15 0.300 4.12
+-5.0 0.565 4.77 3.99 3.15 0.325 5.41
+-5.0 0.565 4.77 3.99 3.15 0.350 7.87
+-5.0 0.565 4.77 3.99 3.15 0.375 12.71
+-5.0 0.565 4.77 3.99 3.15 0.400 21.02
+-5.0 0.565 4.77 3.99 3.15 0.425 34.58
+-5.0 0.565 4.77 3.99 3.15 0.450 51.77
+0.0 0.565 5.10 3.94 3.51 0.125 0.08
+0.0 0.565 5.10 3.94 3.51 0.150 0.26
+0.0 0.565 5.10 3.94 3.51 0.175 0.50
+0.0 0.565 5.10 3.94 3.51 0.200 0.83
+0.0 0.565 5.10 3.94 3.51 0.225 1.28
+0.0 0.565 5.10 3.94 3.51 0.250 1.90
+0.0 0.565 5.10 3.94 3.51 0.275 2.68
+0.0 0.565 5.10 3.94 3.51 0.300 3.76
+0.0 0.565 5.10 3.94 3.51 0.325 5.57
+0.0 0.565 5.10 3.94 3.51 0.350 8.76
+0.0 0.565 5.10 3.94 3.51 0.375 14.24
+0.0 0.565 5.10 3.94 3.51 0.400 23.05
+0.0 0.565 5.10 3.94 3.51 0.425 35.46
+0.0 0.565 5.10 3.94 3.51 0.450 51.99
+-5.0 0.565 5.10 3.94 3.51 0.125 0.08
+-5.0 0.565 5.10 3.94 3.51 0.150 0.24
+-5.0 0.565 5.10 3.94 3.51 0.175 0.45
+-5.0 0.565 5.10 3.94 3.51 0.200 0.77
+-5.0 0.565 5.10 3.94 3.51 0.225 1.19
+-5.0 0.565 5.10 3.94 3.51 0.250 1.76
+-5.0 0.565 5.10 3.94 3.51 0.275 2.59
+-5.0 0.565 5.10 3.94 3.51 0.300 3.85
+-5.0 0.565 5.10 3.94 3.51 0.325 5.27
+-5.0 0.565 5.10 3.94 3.51 0.350 7.74
+-5.0 0.565 5.10 3.94 3.51 0.375 12.40
+-5.0 0.565 5.10 3.94 3.51 0.400 20.91
+-5.0 0.565 5.10 3.94 3.51 0.425 33.23
+-5.0 0.565 5.10 3.94 3.51 0.450 49.14
+-2.3 0.530 5.11 3.69 3.51 0.125 0.08
+-2.3 0.530 5.11 3.69 3.51 0.150 0.25
+-2.3 0.530 5.11 3.69 3.51 0.175 0.46
+-2.3 0.530 5.11 3.69 3.51 0.200 0.75
+-2.3 0.530 5.11 3.69 3.51 0.225 1.11
+-2.3 0.530 5.11 3.69 3.51 0.250 1.57
+-2.3 0.530 5.11 3.69 3.51 0.275 2.17
+-2.3 0.530 5.11 3.69 3.51 0.300 2.98
+-2.3 0.530 5.11 3.69 3.51 0.325 4.42
+-2.3 0.530 5.11 3.69 3.51 0.350 7.84
+-2.3 0.530 5.11 3.69 3.51 0.375 14.11
+-2.3 0.530 5.11 3.69 3.51 0.400 24.14
+-2.3 0.530 5.11 3.69 3.51 0.425 37.95
+-2.3 0.530 5.11 3.69 3.51 0.450 55.17
+-2.3 0.530 4.76 3.68 3.16 0.125 0.10
+-2.3 0.530 4.76 3.68 3.16 0.150 0.23
+-2.3 0.530 4.76 3.68 3.16 0.175 0.47
+-2.3 0.530 4.76 3.68 3.16 0.200 0.76
+-2.3 0.530 4.76 3.68 3.16 0.225 1.15
+-2.3 0.530 4.76 3.68 3.16 0.250 1.65
+-2.3 0.530 4.76 3.68 3.16 0.275 2.28
+-2.3 0.530 4.76 3.68 3.16 0.300 3.09
+-2.3 0.530 4.76 3.68 3.16 0.325 4.41
+-2.3 0.530 4.76 3.68 3.16 0.350 7.51
+-2.3 0.530 4.76 3.68 3.16 0.375 13.77
+-2.3 0.530 4.76 3.68 3.16 0.400 23.96
+-2.3 0.530 4.76 3.68 3.16 0.425 37.38
+-2.3 0.530 4.76 3.68 3.16 0.450 56.46
+-2.3 0.530 4.34 2.81 3.15 0.125 0.05
+-2.3 0.530 4.34 2.81 3.15 0.150 0.17
+-2.3 0.530 4.34 2.81 3.15 0.175 0.35
+-2.3 0.530 4.34 2.81 3.15 0.200 0.63
+-2.3 0.530 4.34 2.81 3.15 0.225 1.01
+-2.3 0.530 4.34 2.81 3.15 0.250 1.43
+-2.3 0.530 4.34 2.81 3.15 0.275 2.05
+-2.3 0.530 4.34 2.81 3.15 0.300 2.73
+-2.3 0.530 4.34 2.81 3.15 0.325 3.87
+-2.3 0.530 4.34 2.81 3.15 0.350 7.19
+-2.3 0.530 4.34 2.81 3.15 0.375 13.96
+-2.3 0.530 4.34 2.81 3.15 0.400 25.18
+-2.3 0.530 4.34 2.81 3.15 0.425 41.34
+-2.3 0.530 4.34 2.81 3.15 0.450 62.42
+0.0 0.600 4.78 4.24 3.15 0.125 0.03
+0.0 0.600 4.78 4.24 3.15 0.150 0.18
+0.0 0.600 4.78 4.24 3.15 0.175 0.40
+0.0 0.600 4.78 4.24 3.15 0.200 0.73
+0.0 0.600 4.78 4.24 3.15 0.225 1.30
+0.0 0.600 4.78 4.24 3.15 0.250 2.16
+0.0 0.600 4.78 4.24 3.15 0.275 3.35
+0.0 0.600 4.78 4.24 3.15 0.300 5.06
+0.0 0.600 4.78 4.24 3.15 0.325 7.14
+0.0 0.600 4.78 4.24 3.15 0.350 10.36
+0.0 0.600 4.78 4.24 3.15 0.375 15.25
+0.0 0.600 4.78 4.24 3.15 0.400 23.15
+0.0 0.600 4.78 4.24 3.15 0.425 34.62
+0.0 0.600 4.78 4.24 3.15 0.450 51.50
+-5.0 0.600 4.78 4.24 3.15 0.125 0.06
+-5.0 0.600 4.78 4.24 3.15 0.150 0.15
+-5.0 0.600 4.78 4.24 3.15 0.175 0.34
+-5.0 0.600 4.78 4.24 3.15 0.200 0.63
+-5.0 0.600 4.78 4.24 3.15 0.225 1.13
+-5.0 0.600 4.78 4.24 3.15 0.250 1.85
+-5.0 0.600 4.78 4.24 3.15 0.275 2.84
+-5.0 0.600 4.78 4.24 3.15 0.300 4.34
+-5.0 0.600 4.78 4.24 3.15 0.325 6.20
+-5.0 0.600 4.78 4.24 3.15 0.350 8.62
+-5.0 0.600 4.78 4.24 3.15 0.375 12.49
+-5.0 0.600 4.78 4.24 3.15 0.400 20.41
+-5.0 0.600 4.78 4.24 3.15 0.425 32.46
+-5.0 0.600 4.78 4.24 3.15 0.450 50.94
+0.0 0.530 4.78 3.75 3.15 0.125 0.16
+0.0 0.530 4.78 3.75 3.15 0.150 0.32
+0.0 0.530 4.78 3.75 3.15 0.175 0.59
+0.0 0.530 4.78 3.75 3.15 0.200 0.92
+0.0 0.530 4.78 3.75 3.15 0.225 1.37
+0.0 0.530 4.78 3.75 3.15 0.250 1.94
+0.0 0.530 4.78 3.75 3.15 0.275 2.62
+0.0 0.530 4.78 3.75 3.15 0.300 3.70
+0.0 0.530 4.78 3.75 3.15 0.325 5.45
+0.0 0.530 4.78 3.75 3.15 0.350 9.45
+0.0 0.530 4.78 3.75 3.15 0.375 16.31
+0.0 0.530 4.78 3.75 3.15 0.400 27.34
+0.0 0.530 4.78 3.75 3.15 0.425 41.77
+0.0 0.530 4.78 3.75 3.15 0.450 60.85
+-5.0 0.530 4.78 3.75 3.15 0.125 0.09
+-5.0 0.530 4.78 3.75 3.15 0.150 0.24
+-5.0 0.530 4.78 3.75 3.15 0.175 0.47
+-5.0 0.530 4.78 3.75 3.15 0.200 0.78
+-5.0 0.530 4.78 3.75 3.15 0.225 1.21
+-5.0 0.530 4.78 3.75 3.15 0.250 1.85
+-5.0 0.530 4.78 3.75 3.15 0.275 2.62
+-5.0 0.530 4.78 3.75 3.15 0.300 3.69
+-5.0 0.530 4.78 3.75 3.15 0.325 5.07
+-5.0 0.530 4.78 3.75 3.15 0.350 7.95
+-5.0 0.530 4.78 3.75 3.15 0.375 13.73
+-5.0 0.530 4.78 3.75 3.15 0.400 23.55
+-5.0 0.530 4.78 3.75 3.15 0.425 37.14
+-5.0 0.530 4.78 3.75 3.15 0.450 55.87
+-2.3 0.600 5.10 4.17 3.51 0.125 0.01
+-2.3 0.600 5.10 4.17 3.51 0.150 0.16
+-2.3 0.600 5.10 4.17 3.51 0.175 0.39
+-2.3 0.600 5.10 4.17 3.51 0.200 0.73
+-2.3 0.600 5.10 4.17 3.51 0.225 1.24
+-2.3 0.600 5.10 4.17 3.51 0.250 1.96
+-2.3 0.600 5.10 4.17 3.51 0.275 3.04
+-2.3 0.600 5.10 4.17 3.51 0.300 4.46
+-2.3 0.600 5.10 4.17 3.51 0.325 6.31
+-2.3 0.600 5.10 4.17 3.51 0.350 8.68
+-2.3 0.600 5.10 4.17 3.51 0.375 12.39
+-2.3 0.600 5.10 4.17 3.51 0.400 20.14
+-2.3 0.600 5.10 4.17 3.51 0.425 31.77
+-2.3 0.600 5.10 4.17 3.51 0.450 47.13
+-2.3 0.600 4.34 4.23 2.73 0.125 0.04
+-2.3 0.600 4.34 4.23 2.73 0.150 0.17
+-2.3 0.600 4.34 4.23 2.73 0.175 0.36
+-2.3 0.600 4.34 4.23 2.73 0.200 0.64
+-2.3 0.600 4.34 4.23 2.73 0.225 1.02
+-2.3 0.600 4.34 4.23 2.73 0.250 1.62
+-2.3 0.600 4.34 4.23 2.73 0.275 2.63
+-2.3 0.600 4.34 4.23 2.73 0.300 4.15
+-2.3 0.600 4.34 4.23 2.73 0.325 6.00
+-2.3 0.600 4.34 4.23 2.73 0.350 8.47
+-2.3 0.600 4.34 4.23 2.73 0.375 12.27
+-2.3 0.600 4.34 4.23 2.73 0.400 19.59
+-2.3 0.600 4.34 4.23 2.73 0.425 30.48
+-2.3 0.600 4.34 4.23 2.73 0.450 46.66
+

requirements.txt

@@ -0,0 +1,9 @@
+numpy
+pandas
+scikit-learn
+pandas
+matplotlib
+seaborn
+torch
+torchvision
+monotonenorm