initial commit

app.py

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+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.neighbors import kneighbors_graph
+
+import gradio as gr
+
+
+np.random.seed(42)
+
+def agglomorative_cluster(n_samples: int, n_neighbours: int, n_clusters: int, linkage: str, connectivity: bool) -> "plt.Figure":
+
+    t = 1.5 * np.pi * (1 + 3 * np.random.rand(1, n_samples))
+    x = t * np.cos(t)
+    y = t * np.sin(t)
+
+    X = np.concatenate((x, y))
+    X += 0.7 * np.random.randn(2, n_samples)
+    X = X.T
+
+    knn_graph = kneighbors_graph(X, n_neighbors=n_neighbours, include_self=False)
+    connectivity = knn_graph if not connectivity else None
+
+    fig, ax = plt.subplots(1, 1, figsize=(24, 15))
+    model = AgglomerativeClustering(linkage=linkage, connectivity=connectivity, n_clusters=int(n_clusters))
+    model.fit(X)
+    ax.scatter(X[:, 0], X[:, 1], c=model.labels_, cmap=plt.cm.nipy_spectral)
+    ax.axis("equal")
+    ax.axis("off")
+
+    return fig
+
+
+
+
+
+demo = gr.Interface(
+    fn = agglomorative_cluster,
+    inputs = [gr.Slider(0, 20_000, label="n_samples", info="the number of samples in the data.", step=1), 
+              gr.Slider(0, 30, label="n_neighbours", info="the number of neighbours in the data", step=1), 
+              gr.Dropdown([3, 30], label="n_clusters", info="the number of clusters in the data"), 
+              gr.Dropdown(['average', 'complete', 'ward', 'single'], label="linkage", info="the different types of aggolomorative clustering techniques"),
+              gr.Checkbox(True, label="connectivity", info="whether to impose a connectivity into the graph")],
+    
+    outputs = [gr.Plot(label="Plot")]
+)
+
+demo.launch()
+

requirements.txt

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+scikit-learn
+scipy
+numpy
+pandas
+matplotlib