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Update app.py
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app.py
CHANGED
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@@ -1,7 +1,7 @@
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import numpy as np
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import streamlit as st
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import matplotlib.pyplot as plt
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plt.style.use('fivethirtyeight')
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st.subheader("Gaussian Process Regression with Squared Exponential Kernel")
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@@ -30,7 +30,7 @@ def rbf_kernel(x1, x2, lengthscale, variance):
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dist_sqr = (x1_ - x2_) ** 2
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return variance * np.exp(-dist_sqr)
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fig, ax = plt.subplots(figsize=(10,4)
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x_train = np.array([0.2, 0.5, 0.8]).reshape(-1,1)
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y_train = np.array([0.8, 0.3, 0.6]).reshape(-1,1)
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@@ -58,7 +58,6 @@ ax.fill_between(x_test.ravel(), pred_mean.ravel()-pred_std2.ravel(),
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pred_mean.ravel()+pred_std2.ravel(), alpha=0.5, label='$\\mu \\pm 2\\sigma$')
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.set_facecolor('white')
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ax.legend()
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with st_col:
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import numpy as np
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import streamlit as st
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import matplotlib.pyplot as plt
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# plt.style.use('fivethirtyeight')
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st.subheader("Gaussian Process Regression with Squared Exponential Kernel")
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dist_sqr = (x1_ - x2_) ** 2
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return variance * np.exp(-dist_sqr)
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fig, ax = plt.subplots(figsize=(10,4))
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x_train = np.array([0.2, 0.5, 0.8]).reshape(-1,1)
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y_train = np.array([0.8, 0.3, 0.6]).reshape(-1,1)
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pred_mean.ravel()+pred_std2.ravel(), alpha=0.5, label='$\\mu \\pm 2\\sigma$')
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ax.set_xlabel('x')
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ax.set_ylabel('y')
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ax.legend()
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with st_col:
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