Upload app.py

app.py

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+import numpy as np
+import pandas as pd
+
+def compute_line_parameters(slope, intercept, mean, std, num_points, x_min, x_max, num_iterations):
+    line_parameters = np.zeros((num_iterations, 2))
+    for i in range(num_iterations):
+        # Generate points on the line with noise
+        x_values, y_values_with_noise = predict_line_with_noise(slope, intercept, mean, std, num_points, x_min, x_max)
+
+        # Randomly sample two points from the line
+        idx = np.random.choice(num_points, 2, replace=False)
+        x1, y1 = x_values[idx[0]], y_values_with_noise[idx[0]]
+        x2, y2 = x_values[idx[1]], y_values_with_noise[idx[1]]
+
+        # Compute slope and intercept of line passing through sampled points
+        line_slope = (y2 - y1) / (x2 - x1)
+        line_intercept = y1 - line_slope * x1
+
+        line_parameters[i, 0] = line_slope
+        line_parameters[i, 1] = line_intercept
+
+    return line_parameters
+
+from sklearn.cluster import KMeans
+# import matplotlib.pyplot as plt
+
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.cluster import KMeans
+
+def cluster_line_parameters(line_parameters, num_clusters):
+    # Cluster line parameters using KMeans
+    kmeans = KMeans(n_clusters=num_clusters)
+    kmeans.fit(line_parameters)
+    labels = kmeans.labels_
+    centroids = kmeans.cluster_centers_
+    # how many points are in each cluster
+    counts = np.bincount(labels)
+
+    # Create a scatter plot of the data points
+    fig, ax = plt.subplots()
+    ax.scatter(line_parameters[:, 0], line_parameters[:, 1], c=labels, cmap='viridis')
+    ax.set_xlabel('Slope')
+    ax.set_ylabel('Intercept')
+
+    # Generate some lines using the centroids
+    '''
+    x = np.linspace(x_min, x_max, num_points)
+    for i in range(num_clusters):
+        slope, intercept = centroids[i]
+        y = slope * x + intercept
+        ax.plot(x, y, linewidth=2)
+    '''
+
+    # Convert the figure to a PNG image
+    fig.canvas.draw()
+    img = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)
+    img = img.reshape(fig.canvas.get_width_height()[::-1] + (3,))
+
+    # Close the figure to free up memory
+    plt.close(fig)
+
+    # Return the labels, centroids, and image
+    return labels, centroids, counts, img
+
+import gradio as gr
+
+def predict_line_with_noise(slope, intercept, mean, std, num_points, x_min, x_max):
+    x_values = np.linspace(x_min, x_max, num_points)
+    noise = np.random.normal(mean, std, num_points)
+    y_values = slope * x_values + intercept + noise
+    return x_values, y_values
+
+def cluster_line_params(slope, intercept, mean, std, num_points, x_min, x_max, num_iterations, num_clusters):
+    num_points = int(num_points)
+    num_iterations = int(num_iterations)
+    num_clusters = int(num_clusters)
+    line_parameters = compute_line_parameters(slope, intercept, mean, std, num_points, x_min, x_max, num_iterations)
+    labels, centroids, counts, img = cluster_line_parameters(line_parameters, num_clusters)
+    # return labels, centroids, img
+    # put counts as the third column of centroids
+    centroids = np.c_[centroids, counts]
+    df = pd.DataFrame(centroids, columns=["Slope", "Intercept", "Count"])
+    # return img, centroids, df
+    return img, df
+
+# Define input and output components
+inputs = [
+    gr.inputs.Slider(minimum=-5.0, maximum=5.0, default=1.0, label="Slope"),
+    gr.inputs.Slider(minimum=-10.0, maximum=10.0, default=0.0, label="Intercept"),
+    gr.inputs.Slider(minimum=0.0, maximum=5.0, default=1.0, label="Mean"),
+    gr.inputs.Slider(minimum=0.0, maximum=5.0, default=1.0, label="Standard Deviation"),
+    gr.inputs.Number(default=100, label="Number of Points"),
+    gr.inputs.Number(default=-5, label="Minimum Value of x"),
+    gr.inputs.Number(default=5, label="Maximum Value of x"),
+    gr.inputs.Number(default=100, label="Number of Iterations"),
+    gr.inputs.Number(default=3, label="Number of Clusters")
+]
+
+outputs = [
+    #image and numpy
+    gr.outputs.Image(label="Image", type="pil"),
+    # show the centroids and counts, which is a numpy array
+    gr.outputs.Dataframe(label="Centroids and Counts", type="pandas")
+]
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=cluster_line_params,
+    inputs=inputs,
+    # outputs=["numpy", "numpy", "image"],
+    outputs=outputs,
+    title="Line Parameter Clustering",
+    description="Cluster line parameters with Gaussian noise",
+    allow_flagging=False
+)
+
+# Launch the interface
+interface.launch(share=True)
+
+
+