Demonstrates the effect of different metrics on the hierarchical clustering. The example is engineered to show the effect of the choice of different metrics. It is applied to waveforms, which can be seen as high-dimensional vector. Indeed, the difference between metrics is usually more pronounced in high dimension (in particular for euclidean and cityblock). We generate data from three groups of waveforms. Two of the waveforms (waveform 1 and waveform 2) are proportional one to the other. The

class sklearn.cluster.AgglomerativeClustering(n_clusters=2, affinity='euclidean', memory=Memory(cachedir=None), connectivity=None, compute_full_tree='auto', linkage='ward', pooling_func=) [source] Agglomerative Clustering Recursively merges the pair of clusters that minimally increases a given linkage distance. Read more in the User Guide. Parameters: n_clusters : int, default=2 The number of clusters to find. connectivity : array-like or callable, optional Connectivity matrix. Defines

class sklearn.semi_supervised.LabelPropagation(kernel='rbf', gamma=20, n_neighbors=7, alpha=1, max_iter=30, tol=0.001, n_jobs=1) [source] Label Propagation classifier Read more in the User Guide. Parameters: kernel : {?knn?, ?rbf?} String identifier for kernel function to use. Only ?rbf? and ?knn? kernels are currently supported.. gamma : float Parameter for rbf kernel n_neighbors : integer > 0 Parameter for knn kernel alpha : float Clamping factor max_iter : float Change maxim

class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100, ), activation='relu', solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08) [source] Multi-layer Perceptron classifier. This model optimizes the log-

sklearn.cluster.ward_tree(X, connectivity=None, n_clusters=None, return_distance=False) [source] Ward clustering based on a Feature matrix. Recursively merges the pair of clusters that minimally increases within-cluster variance. The inertia matrix uses a Heapq-based representation. This is the structured version, that takes into account some topological structure between samples. Read more in the User Guide. Parameters: X : array, shape (n_samples, n_features) feature matrix representing

sklearn.feature_selection.f_regression(X, y, center=True) [source] Univariate linear regression tests. Quick linear model for testing the effect of a single regressor, sequentially for many regressors. This is done in 2 steps: The cross correlation between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) * (y - mean_y)) / (std(X[:, i]) * std(y)). It is converted to an F score then to a p-value. Read more in the User Guide. Parameters: X : {array-like, sparse m

Simple usage of various cross decomposition algorithms: - PLSCanonical - PLSRegression, with multivariate response, a.k.a. PLS2 - PLSRegression, with univariate response, a.k.a. PLS1 - CCA Given 2 multivariate covarying two-dimensional datasets, X, and Y, PLS extracts the ?directions of covariance?, i.e. the components of each datasets that explain the most shared variance between both datasets. This is apparent on the scatterplot matrix display: components 1 in dataset X and dataset Y are max

sklearn.datasets.fetch_20newsgroups(data_home=None, subset='train', categories=None, shuffle=True, random_state=42, remove=(), download_if_missing=True) [source] Load the filenames and data from the 20 newsgroups dataset. Read more in the User Guide. Parameters: subset : ?train? or ?test?, ?all?, optional Select the dataset to load: ?train? for the training set, ?test? for the test set, ?all? for both, with shuffled ordering. data_home : optional, default: None Specify a download and ca

class sklearn.neural_network.BernoulliRBM(n_components=256, learning_rate=0.1, batch_size=10, n_iter=10, verbose=0, random_state=None) [source] Bernoulli Restricted Boltzmann Machine (RBM). A Restricted Boltzmann Machine with binary visible units and binary hidden units. Parameters are estimated using Stochastic Maximum Likelihood (SML), also known as Persistent Contrastive Divergence (PCD) [2]. The time complexity of this implementation is O(d ** 2) assuming d ~ n_features ~ n_components.

sklearn.manifold.spectral_embedding(adjacency, n_components=8, eigen_solver=None, random_state=None, eigen_tol=0.0, norm_laplacian=True, drop_first=True) [source] Project the sample on the first eigenvectors of the graph Laplacian. The adjacency matrix is used to compute a normalized graph Laplacian whose spectrum (especially the eigenvectors associated to the smallest eigenvalues) has an interpretation in terms of minimal number of cuts necessary to split the graph into comparably sized co