Update app.py

app.py

@@ -9,7 +9,8 @@ def compare_manifold_learning(methods, n_samples, n_neighbors, n_components, per
     S_points, S_color = datasets.make_s_curve(n_samples, random_state=0)
     transformed_data = []
 
-    for method in methods:
+    if len(methods) == 1:
+        method = methods[0]
         manifold_method = {
             "Locally Linear Embeddings Standard": manifold.LocallyLinearEmbedding(method="standard", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
             "Locally Linear Embeddings LTSA": manifold.LocallyLinearEmbedding(method="ltsa", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
@@ -22,17 +23,42 @@ def compare_manifold_learning(methods, n_samples, n_neighbors, n_components, per
         }[method]
         S_transformed = manifold_method.fit_transform(S_points)
         transformed_data.append(S_transformed)
+    else:
+        for method in methods:
+            manifold_method = {
+                "Locally Linear Embeddings Standard": manifold.LocallyLinearEmbedding(method="standard", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
+                "Locally Linear Embeddings LTSA": manifold.LocallyLinearEmbedding(method="ltsa", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
+                "Locally Linear Embeddings Hessian": manifold.LocallyLinearEmbedding(method="hessian", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
+                "Locally Linear Embeddings Modified": manifold.LocallyLinearEmbedding(method="modified", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
+                "Isomap": manifold.Isomap(n_neighbors=n_neighbors, n_components=n_components, p=1),
+                "MultiDimensional Scaling": manifold.MDS(n_components=n_components, max_iter=50, n_init=4, random_state=0, normalized_stress=False),
+                "Spectral Embedding": manifold.SpectralEmbedding(n_components=n_components, n_neighbors=n_neighbors),
+                "T-distributed Stochastic Neighbor Embedding": manifold.TSNE(n_components=n_components, perplexity=perplexity, init="random", n_iter=250, random_state=0)
+            }[method]
+            S_transformed = manifold_method.fit_transform(S_points)
+            transformed_data.append(S_transformed)
 
     fig, axs = plt.subplots(1, len(transformed_data), figsize=(6 * len(transformed_data), 6))
     fig.suptitle("Manifold Learning Comparison", fontsize=16)
 
-    for ax, method, data in zip(axs, methods, transformed_data):
+    if len(methods) == 1:
+        ax = axs
+        method = methods[0]
+        data = transformed_data[0]
         ax.scatter(data[:, 0], data[:, 1], c=S_color, cmap=plt.cm.Spectral)
         ax.set_title(f"Method: {method}")
         ax.axis("tight")
         ax.axis("off")
         ax.xaxis.set_major_locator(ticker.NullLocator())
         ax.yaxis.set_major_locator(ticker.NullLocator())
+    else:
+        for ax, method, data in zip(axs, methods, transformed_data):
+            ax.scatter(data[:, 0], data[:, 1], c=S_color, cmap=plt.cm.Spectral)
+            ax.set_title(f"Method: {method}")
+            ax.axis("tight")
+            ax.axis("off")
+            ax.xaxis.set_major_locator(ticker.NullLocator())
+            ax.yaxis.set_major_locator(ticker.NullLocator())
 
     plt.tight_layout()
     plt.savefig("plot.png")
@@ -41,14 +67,14 @@ def compare_manifold_learning(methods, n_samples, n_neighbors, n_components, per
     return "plot.png"
 
 method_options = [
-    "Locally Linear Embeddings Standard",
-    "Locally Linear Embeddings LTSA",
-    "Locally Linear Embeddings Hessian",
-    "Locally Linear Embeddings Modified",
+    "LLE Standard",
+    "LLE LTSA",
+    "LLE Hessian",
+    "LLE Modified",
     "Isomap",
-    "MultiDimensional Scaling",
+    "MDS",
     "Spectral Embedding",
-    "T-distributed Stochastic Neighbor Embedding"
+    "t-SNE"
 ]
 
 inputs = [