Update app.py

app.py

@@ -11,14 +11,14 @@ def compare_manifold_learning(methods, n_samples, n_neighbors, n_components, per
 
     for method in methods:
         manifold_method = {
-            "LLE Standard": manifold.LocallyLinearEmbedding(method="standard", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
-            "LLE LTSA": manifold.LocallyLinearEmbedding(method="ltsa", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
-            "LLE Hessian": manifold.LocallyLinearEmbedding(method="hessian", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
-            "LLE Modified": manifold.LocallyLinearEmbedding(method="modified", n_neighbors=n_neighbors, n_components=n_components, eigen_solver="auto", random_state=0),
+            "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),
-            "MDS": manifold.MDS(n_components=n_components, max_iter=50, n_init=4, random_state=0, normalized_stress=False),
+            "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-SNE": manifold.TSNE(n_components=n_components, perplexity=perplexity, init="random", n_iter=250, random_state=0)
+            "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)
@@ -41,14 +41,14 @@ def compare_manifold_learning(methods, n_samples, n_neighbors, n_components, per
     return "plot.png"
 
 method_options = [
-    "LLE Standard",
-    "LLE LTSA",
-    "LLE Hessian",
-    "LLE Modified",
+    "Locally Linear Embeddings Standard",
+    "Locally Linear Embeddings LTSA",
+    "Locally Linear Embeddings Hessian",
+    "Locally Linear Embeddings Modified",
     "Isomap",
-    "MDS",
+    "MultiDimensional Scaling",
     "Spectral Embedding",
-    "t-SNE"
+    "T-distributed Stochastic Neighbor Embedding"
 ]
 
 inputs = [