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import streamlit as st | |
import numpy as np | |
import matplotlib.pyplot as plt | |
st.title('Fitting simple models with JAX') | |
st.header('A quadratric regression example') | |
number_of_observations = st.sidebar.slider('Number of observations', min_value=50, max_value=150, value=50) | |
noise_standard_deviation = st.sidebar.slider('Standard deviation of the noise', min_value = 0.0, max_value=2.0, value=0.25) | |
np.random.seed(2) | |
X = np.column_stack((np.ones(number_of_observations), | |
np.random.random(number_of_observations))) | |
w = np.array([3.0, -20.0, 32.0]) # coefficients | |
X = np.column_stack((X, X[:,1] ** 2)) # add x**2 column | |
additional_noise = 8 * np.random.binomial(1, 0.03, size = number_of_observations) | |
y = np.dot(X, w) + noise_standard_deviation * np.random.randn(number_of_observations) \ | |
+ additional_noise | |
st.subheader('Quadratic regression') | |
fig, ax = plt.subplots(dpi=320) | |
ax.set_xlim((0,1)) | |
ax.set_ylim((-5,20)) | |
ax.scatter(X[:,1], y, c='r', edgecolors='black') | |
st.pyplot(fig) | |
st.write(X[:5, :]) |