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Results 1 - 10 of 106 for Ward (0.2 sec)

  1. benchmarks/bench_plot_ward.py

    """
    Benchmark scikit-learn's Ward implement compared to SciPy's
    """
    
    import time
    
    import numpy as np
    from scipy.cluster import hierarchy
    import matplotlib.pyplot as plt
    
    from sklearn.cluster import AgglomerativeClustering
    
    ward = AgglomerativeClustering(n_clusters=3, linkage='ward')
    
    n_samples = np.logspace(.5, 3, 9)
    n_features = np.logspace(1, 3.5, 7)
    N_samples, N_features = np.meshgrid(n_samples,
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-06-24 14:51
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  2. examples/cluster/plot_coin_ward_segmentation.py

    # Compute clustering
    print("Compute structured hierarchical clustering...")
    st = time.time()
    n_clusters = 27  # number of regions
    ward = AgglomerativeClustering(n_clusters=n_clusters, linkage='ward',
                                   connectivity=connectivity)
    ward.fit(X)
    label = np.reshape(ward.labels_, rescaled_coins.shape)
    print("Elapsed time: ", time.time() - st)
    print("Number of pixels: ", label.size)
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-06-24 11:35
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  3. examples/cluster/plot_ward_structured_vs_unstructured.py

    # #############################################################################
    # Compute clustering
    print("Compute unstructured hierarchical clustering...")
    st = time.time()
    ward = AgglomerativeClustering(n_clusters=6, linkage='ward').fit(X)
    elapsed_time = time.time() - st
    label = ward.labels_
    print("Elapsed time: %.2fs" % elapsed_time)
    print("Number of points: %i" % label.size)
    
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-06-24 14:51
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  4. sklearn/cluster/tests/test_hierarchical.py

        ward = AgglomerativeClustering(
            n_clusters=4, connectivity=connectivity, linkage='ward')
        # If changes are not propagated correctly, fit crashes with an
        # IndexError
        ward.fit(X)
    
    
    def test_ward_tree_children_order():
        # Check that children are ordered in the same way for both structured and
        # unstructured versions of ward_tree.
    
        # test on five random datasets
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-07-26 13:24
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  5. .github/actions/high-priority-prs/src/__tests__/__snapshots__/pr-message.js.snap

    8. *<https://github.com/gatsbyjs/gatsby/pull/11947|Add resolvePaths, get webpack instance to recognize react and es6 syntax>* — _created_ 3 months ago — _updated_ about 1 month ago — commented: <@Ward>
    Plain Text
    - Registered: 2020-10-26 17:52
    - Last Modified: 2019-06-06 16:00
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  6. examples/cluster/plot_linkage_comparison.py

        # normalize dataset for easier parameter selection
        X = StandardScaler().fit_transform(X)
    
        # ============
        # Create cluster objects
        # ============
        ward = cluster.AgglomerativeClustering(
            n_clusters=params['n_clusters'], linkage='ward')
        complete = cluster.AgglomerativeClustering(
            n_clusters=params['n_clusters'], linkage='complete')
        average = cluster.AgglomerativeClustering(
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-06-24 14:51
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  7. sklearn/cluster/_agglomerative.py

    
    _TREE_BUILDERS = dict(
        ward=ward_tree,
        complete=_complete_linkage,
        average=_average_linkage,
        single=_single_linkage)
    
    
    ###############################################################################
    # Functions for cutting hierarchical clustering tree
    
    def _hc_cut(n_clusters, children, n_leaves):
        """Function cutting the ward tree for a given number of clusters.
    
        Parameters
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-09-04 14:35
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  8. examples/cluster/plot_feature_agglomeration_vs_univariate_selection.py

    mem = Memory(location=cachedir, verbose=1)
    
    # Ward agglomeration followed by BayesianRidge
    connectivity = grid_to_graph(n_x=size, n_y=size)
    ward = FeatureAgglomeration(n_clusters=10, connectivity=connectivity,
                                memory=mem)
    clf = Pipeline([('ward', ward), ('ridge', ridge)])
    # Select the optimal number of parcels with grid search
    clf = GridSearchCV(clf, {'ward__n_clusters': [10, 20, 30]}, n_jobs=1, cv=cv)
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-06-24 14:51
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  9. runtime/ftplugin/rst.vim

    " reStructuredText filetype plugin file
    " Language: reStructuredText documentation format
    " Maintainer: Marshall Ward <marshall.ward@gmail.com>
    " Original Maintainer: Nikolai Weibull <******@****.***>
    " Website: https://github.com/marshallward/vim-restructuredtext
    " Latest Revision: 2018-12-29
    
    if exists("b:did_ftplugin")
        finish
    endif
    let b:did_ftplugin = 1
    
    let s:cpo_save = &cpo
    set cpo&vim
    
    "Disable folding
    Plain Text
    - Registered: 2020-10-26 08:44
    - Last Modified: 2019-08-01 14:51
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  10. examples/cluster/plot_cluster_comparison.py

        # ============
        ms = cluster.MeanShift(bandwidth=bandwidth, bin_seeding=True)
        two_means = cluster.MiniBatchKMeans(n_clusters=params['n_clusters'])
        ward = cluster.AgglomerativeClustering(
            n_clusters=params['n_clusters'], linkage='ward',
            connectivity=connectivity)
        spectral = cluster.SpectralClustering(
            n_clusters=params['n_clusters'], eigen_solver='arpack',
    Python
    - Registered: 2020-10-16 09:24
    - Last Modified: 2020-06-24 14:51
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