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c98136b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import numpy as np
import gradio as gr
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
def initialize_input(N, std):
x = np.random.uniform(0, 1, N)
x.sort()
noise = np.random.normal(0, std, N)
fx = lambda input: np.sin(2 * np.pi * input)
y = fx(x) + noise
return x, y, fx
def fit_poly(X, y, M, regularization):
lam = np.exp(regularization)
X = X[:, np.newaxis]
poly = PolynomialFeatures(degree=M)
X_poly = poly.fit_transform(X)
model = Ridge(alpha=lam)
model.fit(X_poly, y)
return model, poly
def plot_and_error(M, N, std, seed, regularization, show_truth):
np.random.seed(int(seed))
# Prepare data
X, y, fx = initialize_input(N, std)
model, poly = fit_poly(X, y, M, regularization)
# Plot curve
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(X, y, 'o', mfc='none', mec='b', ms=6, label="Data")
X_truth = np.linspace(0, 1, 400)
y_hat = model.predict(poly.transform(X_truth[:, np.newaxis]))
if show_truth:
ax.plot(X_truth, np.sin(2 * np.pi * X_truth), color='lightgreen', lw=2, label=r"$\sin(2\pi x)$")
ax.plot(X_truth, y_hat, 'r-', label="Predicted polynomial")
ax.set_xlim(0, 1)
ax.set_ylim(-1.1, 1.1)
ax.set_xlabel(r"$x$")
ax.set_ylabel(r"$y$")
ax.set_title(f"Polynomial Fit (M={M})")
ax.legend(frameon=False)
# Compute error
y_pred = model.predict(poly.transform(X[:, np.newaxis]))
mse = mean_squared_error(y, y_pred)
error_text = f"📉 Mean Squared Error (MSE): **{mse:.4f}**"
return fig, error_text
with gr.Blocks(css="""
#title {text-align: center; font-size: 28px; font-weight: bold; margin-bottom: 20px;}
#sliders {padding: 12px; background: #f9f9f9; border-radius: 12px; box-shadow: 0 0 10px rgba(0,0,0,0.1);}
.gradio-container {font-family: 'Segoe UI', sans-serif;}
""") as demo:
gr.Markdown("<div id='title'>🎯 Polynomial Fitting Playground</div>")
with gr.Row():
with gr.Column(elem_id="sliders", scale=1):
M = gr.Slider(0, 15, value=3, step=1, label="Polynomial Degree (M)")
N = gr.Slider(5, 100, value=10, step=1, label="Number of Points (N)")
sigma = gr.Slider(0, 1, value=0.1, step=0.05, label="Noise Level (σ)")
reg = gr.Slider(-50, 0, value=-18, step=1, label="Regularization (ln λ)")
seed = gr.Number(value=100, label="Random Seed", precision=0)
show_truth = gr.Checkbox(value=True, label="Show Ground Truth Function")
with gr.Column(scale=2):
plot_output = gr.Plot()
error_output = gr.Markdown()
# Auto update on input change
inputs = [M, N, sigma, seed, reg, show_truth]
for inp in inputs:
inp.change(fn=plot_and_error, inputs=inputs, outputs=[plot_output, error_output])
if __name__ == "__main__":
demo.launch(share=True)
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