Added TSNE Plot

Data_Plotting/2D_Lattice.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bb137b85f16095f396a65fd9f24327374ca4d855c95c29eb916ab3bfe08f596c
+size 166090

Data_Plotting/Plot_TSNE.py

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+from sklearn.manifold import TSNE
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Latent Feature Cluster for Training Data using T-SNE
+def TSNE_reduction(latent_points, perplexity=30, learning_rate=20):
+    latent_dimensionality = len(latent_points[0])
+    model = TSNE(n_components=2, random_state=0, perplexity=perplexity,
+                 learning_rate=learning_rate)  # Perplexity(5-50) | learning_rate(10-1000)
+    embedding = model
+    # configuring the parameters
+    # the number of components = dimension of the embedded space
+    # default perplexity = 30 " Perplexity balances the attention t-SNE gives to local and global aspects of the data.
+    # It is roughly a guess of the number of close neighbors each point has. ..a denser dataset ... requires higher perplexity value"
+    # default learning rate = 200 "If the learning rate is too high, the data may look like a ‘ball’ with any point
+    # approximately equidistant from its nearest neighbours. If the learning rate is too low,
+    # most points may look compressed in a dense cloud with few outliers."
+    tsne_data = model.fit_transform(
+        latent_points)  # When there are more data points, trainX should be the first couple hundred points so TSNE doesn't take too long
+    x = tsne_data[:, 0]
+    y = tsne_data[:, 1]
+    title = ("T-SNE of Data")
+    return x, y, title, embedding
+
+
+########################################################################################################################
+import pandas as pd
+import json
+
+df = pd.read_csv('2D_Lattice.csv')
+row = 0
+box = df.iloc[row,1]
+array = np.array(json.loads(box))
+
+"""
+# For plotting CSV data
+# define a function to flatten a box
+def flatten_box(box_str):
+    box = json.loads(box_str)
+    return np.array(box).flatten()
+
+
+# apply the flatten_box function to each row of the dataframe and create a list of flattened arrays
+flattened_arrays = df['Array'].apply(flatten_box).tolist()
+
+
+x, y, title, embedding = TSNE_reduction(flattened_arrays)
+
+plt.scatter(x,y)
+plt.title(title)
+plt.show()
+"""
+
+# def plot_dimensionality_reduction(x, y, label_set, title):
+#     plt.title(title)
+#     if label_set[0].dtype == float:
+#         plt.scatter(x, y, c=label_set)
+#         plt.colorbar()
+#         print("using scatter")
+#     else:
+#         for label in set(label_set):
+#             cond = np.where(np.array(label_set) == str(label))
+#             plt.plot(x[cond], y[cond], marker='o', linestyle='none', label=label)
+#
+#         plt.legend(numpoints=1)
+#
+#     plt.show()
+#     plt.close()