Add kernel pca fitting and testing

app.py

@@ -4,4 +4,39 @@ def greet(name):
     return "Hello " + name + "!!"
 
 iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+iface.launch()
+
+from sklearn.datasets import make_circles
+from sklearn.model_selection import train_test_split
+
+X, y = make_circles(n_samples=1_000, factor=0.3, noise=0.05, random_state=0)
+X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=0)
+
+from sklearn.decomposition import PCA, KernelPCA
+
+pca = PCA(n_components=2)
+kernel_pca = KernelPCA(
+    n_components=None, kernel="rbf", gamma=10, fit_inverse_transform=True, alpha=0.1
+)
+
+X_test_pca = pca.fit(X_train).transform(X_test)
+X_test_kernel_pca = kernel_pca.fit(X_train).transform(X_test)
+
+fig, (orig_data_ax, pca_proj_ax, kernel_pca_proj_ax) = plt.subplots(
+    ncols=3, figsize=(14, 4)
+)
+
+orig_data_ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test)
+orig_data_ax.set_ylabel("Feature #1")
+orig_data_ax.set_xlabel("Feature #0")
+orig_data_ax.set_title("Testing data")
+
+pca_proj_ax.scatter(X_test_pca[:, 0], X_test_pca[:, 1], c=y_test)
+pca_proj_ax.set_ylabel("Principal component #1")
+pca_proj_ax.set_xlabel("Principal component #0")
+pca_proj_ax.set_title("Projection of testing data\n using PCA")
+
+kernel_pca_proj_ax.scatter(X_test_kernel_pca[:, 0], X_test_kernel_pca[:, 1], c=y_test)
+kernel_pca_proj_ax.set_ylabel("Principal component #1")
+kernel_pca_proj_ax.set_xlabel("Principal component #0")
+_ = kernel_pca_proj_ax.set_title("Projection of testing data\n using KernelPCA")