8000 Fix typos (#9095) · scikit-learn/scikit-learn@93871e2 · GitHub
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taehoonleelesteve
taehoonlee
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lesteve
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Fix typos (#9095)
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‎doc/modules/neural_networks_unsupervised.rst‎

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@@ -53,7 +53,7 @@ The graphical model of an RBM is a fully-connected bipartite graph.
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The nodes are random variables whose states depend on the state of the other
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nodes they are connected to. The model is therefore parameterized by the
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weights of the connections, as well as one intercept (bias) term for each
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visible and hidden unit, ommited from the image for simplicity.
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visible and hidden unit, omitted from the image for simplicity.
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The energy function measures the quality of a joint assignment:
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‎doc/whats_new.rst‎

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@@ -753,7 +753,7 @@ Classifiers and Regressors
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:issue:`5291` by `Manoj Kumar`_.
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- Added the :class:`multioutput.MultiOutputRegressor` meta-estimator. It
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converts single output regressors to multi-ouput regressors by fitting
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converts single output regressors to multi-output regressors by fitting
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one regressor per output. By :user:`Tim Head <betatim>`.
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Other estimators

‎sklearn/cluster/_k_means_elkan.pyx‎

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@@ -18,7 +18,7 @@ from ..metrics import euclidean_distances
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from ._k_means import _centers_dense
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2020

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cdef floating euclidian_dist(floating* a, floating* b, int n_features) nogil:
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cdef floating euclidean_dist(floating* a, floating* b, int n_features) nogil:
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cdef floating result, tmp
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result = 0
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cdef int i
@@ -89,12 +89,12 @@ cdef update_labels_distances_inplace(
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# assign first cluster center
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c_x = 0
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x = X + sample * n_features
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d_c = euclidian_dist(x, centers, n_features)
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d_c = euclidean_dist(x, centers, n_features)
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lower_bounds[sample, 0] = d_c
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for j in range(1, n_clusters):
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if d_c > center_half_distances[c_x, j]:
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c = centers + j * n_features
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dist = euclidian_dist(x, c, n_features)
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dist = euclidean_dist(x, c, n_features)
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lower_bounds[sample, j] = dist
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if dist < d_c:
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d_c = dist
@@ -197,7 +197,7 @@ def k_means_elkan(np.ndarray[floating, ndim=2, mode='c'] X_, int n_clusters,
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# Recompute the upper bound by calculating the actual distance
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# between the sample and label.
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if not bounds_tight[point_index]:
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upper_bound = euclidian_dist(x_p, centers_p + label * n_features, n_features)
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upper_bound = euclidean_dist(x_p, centers_p + label * n_features, n_features)
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lower_bounds[point_index, label] = upper_bound
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bounds_tight[point_index] = 1
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@@ -206,7 +206,7 @@ def k_means_elkan(np.ndarray[floating, ndim=2, mode='c'] X_, int n_clusters,
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# distance, reassign labels.
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if (upper_bound > lower_bounds[point_index, center_index]
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or (upper_bound > center_half_distances[label, center_index])):
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distance = euclidian_dist(x_p, centers_p + center_index * n_features, n_features)
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distance = euclidean_dist(x_p, centers_p + center_index * n_features, n_features)
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lower_bounds[point_index, center_index] = distance
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if distance < upper_bound:
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label = center_index

‎sklearn/manifold/_barnes_hut_tsne.pyx‎

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Original file line numberDiff line numberDiff line change
@@ -80,7 +80,7 @@ cdef struct Node:
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cdef struct Tree:
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# Holds a pointer to the root node
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Node* root_node
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# Number of dimensions in the ouput
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# Number of dimensions in the output
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int n_dimensions
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# Total number of cells
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long n_cells

‎sklearn/tests/test_multioutput.py‎

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@@ -193,7 +193,7 @@ def test_multi_output_classification_partial_fit():
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assert_array_equal(sgd_linear_clf.predict(X), second_predictions[:, i])
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def test_mutli_output_classifiation_partial_fit_no_first_classes_exception():
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def test_multi_output_classifiation_partial_fit_no_first_classes_exception():
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sgd_linear_clf = SGDClassifier(loss='log', random_state=1)
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multi_target_linear = MultiOutputClassifier(sgd_linear_clf)
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assert_raises_regex(ValueError, "classes must be passed on the first call "

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