scikit-learn
diff --git a/‎dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
-18 Bytes b/‎dev/_downloads/07fcc19ba03226cd3d83d4e40ec44385/auto_examples_python.zip
-18 Bytes
diff --git a/‎dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
-18 Bytes b/‎dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
-18 Bytes
diff --git a/‎dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb
Lines changed: 1 addition & 1 deletion b/‎dev/_downloads/c8db473878b6afea8e75e36dc828f109/plot_compare_methods.ipynb
Lines changed: 1 addition & 1 deletion
diff --git a/‎dev/_downloads/cda53b33015268619bc212d32b7000b9/plot_compare_methods.py
Lines changed: 1 addition & 1 deletion b/‎dev/_downloads/cda53b33015268619bc212d32b7000b9/plot_compare_methods.py
Lines changed: 1 addition & 1 deletion
diff --git a/‎dev/_downloads/scikit-learn-docs.zip
125 KB b/‎dev/_downloads/scikit-learn-docs.zip
125 KB
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
146 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_001.png
146 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
-189 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_002.png
-189 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
37 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_003.png
37 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
141 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_004.png
141 Bytes
diff --git a/‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
52 Bytes b/‎dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png
52 Bytes
@@ -98,7 +98,7 @@
       },
       "outputs": [],
       "source": [
-        "params = {\n    \"n_neighbors\": n_neighbors,\n    \"n_components\": n_components,\n    \"eigen_solver\": \"auto\",\n    \"random_state\": 0,\n}\n\nlle_standard = manifold.LocallyLinearEmbedding(method=\"standard\", **params)\nS_standard = lle_standard.fit_transform(S_points)\n\nlle_ltsa = manifold.LocallyLinearEmbedding(method=\"ltsa\", **params)\nS_ltsa = lle_ltsa.fit_transform(S_points)\n\nlle_hessian = manifold.LocallyLinearEmbedding(method=\"hessian\", **params)\nS_hessian = lle_hessian.fit_transform(S_points)\n\nlle_mod = manifold.LocallyLinearEmbedding(method=\"modified\", modified_tol=0.8, **params)\nS_mod = lle_mod.fit_transform(S_points)"
+        "params = {\n    \"n_neighbors\": n_neighbors,\n    \"n_components\": n_components,\n    \"eigen_solver\": \"auto\",\n    \"random_state\": 0,\n}\n\nlle_standard = manifold.LocallyLinearEmbedding(method=\"standard\", **params)\nS_standard = lle_standard.fit_transform(S_points)\n\nlle_ltsa = manifold.LocallyLinearEmbedding(method=\"ltsa\", **params)\nS_ltsa = lle_ltsa.fit_transform(S_points)\n\nlle_hessian = manifold.LocallyLinearEmbedding(method=\"hessian\", **params)\nS_hessian = lle_hessian.fit_transform(S_points)\n\nlle_mod = manifold.LocallyLinearEmbedding(method=\"modified\", **params)\nS_mod = lle_mod.fit_transform(S_points)"
       ]
     },
     {
 
@@ -119,7 +119,7 @@ def add_2d_scatter(ax, points, points_color, title=None):
 lle_hessian = manifold.LocallyLinearEmbedding(method="hessian", **params)
 S_hessian = lle_hessian.fit_transform(S_points)
 
-lle_mod = manifold.LocallyLinearEmbedding(method="modified", modified_tol=0.8, **params)
+lle_mod = manifold.LocallyLinearEmbedding(method="modified", **params)
 S_mod = lle_mod.fit_transform(S_points)
 
 # %%
Original file line number	Diff line number	Diff line change
`@@ -98,7 +98,7 @@`
`98`	`98`	`},`
`99`	`99`	`"outputs": [],`
`100`	`100`	`"source": [`
`101`		- "params = {\n \"n_neighbors\": n_neighbors,\n \"n_components\": n_components,\n \"eigen_solver\": \"auto\",\n \"random_state\": 0,\n}\n\nlle_standard = manifold.LocallyLinearEmbedding(method=\"standard\", params)\nS_standard = lle_standard.fit_transform(S_points)\n\nlle_ltsa = manifold.LocallyLinearEmbedding(method=\"ltsa\", params)\nS_ltsa = lle_ltsa.fit_transform(S_points)\n\nlle_hessian = manifold.LocallyLinearEmbedding(method=\"hessian\", params)\nS_hessian = lle_hessian.fit_transform(S_points)\n\nlle_mod = manifold.LocallyLinearEmbedding(method=\"modified\", modified_tol=0.8, params)\nS_mod = lle_mod.fit_transform(S_points)"
	`101`	+ "params = {\n \"n_neighbors\": n_neighbors,\n \"n_components\": n_components,\n \"eigen_solver\": \"auto\",\n \"random_state\": 0,\n}\n\nlle_standard = manifold.LocallyLinearEmbedding(method=\"standard\", params)\nS_standard = lle_standard.fit_transform(S_points)\n\nlle_ltsa = manifold.LocallyLinearEmbedding(method=\"ltsa\", params)\nS_ltsa = lle_ltsa.fit_transform(S_points)\n\nlle_hessian = manifold.LocallyLinearEmbedding(method=\"hessian\", params)\nS_hessian = lle_hessian.fit_transform(S_points)\n\nlle_mod = manifold.LocallyLinearEmbedding(method=\"modified\", params)\nS_mod = lle_mod.fit_transform(S_points)"
`102`	`102`	`]`
`103`	`103`	`},`
`104`	`104`	`{`