268 lines
8.7 KiB
Python
268 lines
8.7 KiB
Python
"""
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LDA: Linear Discriminant Analysis
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"""
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# Authors: Matthieu Perrot
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# Mathieu Blondel
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import warnings
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import numpy as np
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from scipy import linalg, ndimage
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from .base import BaseEstimator, ClassifierMixin, TransformerMixin
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from .utils.extmath import logsum
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class LDA(BaseEstimator, ClassifierMixin, TransformerMixin):
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"""
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Linear Discriminant Analysis (LDA)
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Parameters
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----------
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n_components: int
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Number of components (< n_classes - 1)
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priors : array, optional, shape = [n_classes]
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Priors on classes
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Attributes
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----------
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`means_` : array-like, shape = [n_classes, n_features]
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Class means
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`xbar_` : float, shape = [n_features]
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Over all mean
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`priors_` : array-like, shape = [n_classes]
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Class priors (sum to 1)
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`covariance_` : array-like, shape = [n_features, n_features]
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Covariance matrix (shared by all classes)
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Examples
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--------
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>>> import numpy as np
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>>> from sklearn.lda import LDA
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>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
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>>> y = np.array([1, 1, 1, 2, 2, 2])
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>>> clf = LDA()
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>>> clf.fit(X, y)
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LDA(n_components=None, priors=None)
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>>> print clf.predict([[-0.8, -1]])
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[1]
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See also
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--------
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QDA
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"""
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def __init__(self, n_components=None, priors=None):
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self.n_components = n_components
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self.priors = np.asarray(priors) if priors is not None else None
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if self.priors is not None:
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if (self.priors < 0).any():
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raise ValueError('priors must be non-negative')
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if self.priors.sum() != 1:
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print 'warning: the priors do not sum to 1. Renormalizing'
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self.priors = self.priors / self.priors.sum()
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def fit(self, X, y, store_covariance=False, tol=1.0e-4):
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"""
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Fit the LDA model according to the given training data and parameters.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Training vector, where n_samples in the number of samples and
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n_features is the number of features.
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y : array, shape = [n_samples]
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Target values (integers)
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store_covariance : boolean
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If True the covariance matrix (shared by all classes) is computed
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and stored in self.covariance_ attribute.
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"""
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X = np.asanyarray(X)
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y = np.asanyarray(y)
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if y.dtype.char.lower() not in ('b', 'h', 'i'):
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# We need integer values to be able to use
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# ndimage.measurements and np.bincount on numpy >= 2.0.
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# We currently support (u)int8, (u)int16 and (u)int32.
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# Note that versions of scipy >= 0.8 can also accept
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# (u)int64. We however don't support it for backwards
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# compatibility.
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y = y.astype(np.int32)
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if X.ndim != 2:
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raise ValueError('X must be a 2D array')
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if X.shape[0] != y.shape[0]:
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raise ValueError(
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'Incompatible shapes: X has %s samples, while y '
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'has %s' % (X.shape[0], y.shape[0]))
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n_samples = X.shape[0]
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n_features = X.shape[1]
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classes = np.unique(y)
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n_classes = classes.size
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if n_classes < 2:
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raise ValueError('y has less than 2 classes')
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classes_indices = [(y == c).ravel() for c in classes]
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if self.priors is None:
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counts = np.array(ndimage.measurements.sum(
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np.ones(n_samples, dtype=y.dtype), y, index=classes))
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self.priors_ = counts / float(n_samples)
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else:
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self.priors_ = self.priors
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# Group means n_classes*n_features matrix
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means = []
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Xc = []
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cov = None
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if store_covariance:
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cov = np.zeros((n_features, n_features))
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for group_indices in classes_indices:
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Xg = X[group_indices, :]
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meang = Xg.mean(0)
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means.append(meang)
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# centered group data
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Xgc = Xg - meang
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Xc.append(Xgc)
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if store_covariance:
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cov += np.dot(Xgc.T, Xgc)
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if store_covariance:
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cov /= (n_samples - n_classes)
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self.covariance_ = cov
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self.means_ = np.asarray(means)
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Xc = np.concatenate(Xc, 0)
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# ----------------------------
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# 1) within (univariate) scaling by with classes std-dev
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scaling = 1. / Xc.std(0)
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fac = float(1) / (n_samples - n_classes)
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# ----------------------------
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# 2) Within variance scaling
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X = np.sqrt(fac) * (Xc * scaling)
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# SVD of centered (within)scaled data
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U, S, V = linalg.svd(X, full_matrices=0)
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rank = np.sum(S > tol)
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if rank < n_features:
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warnings.warn("Variables are collinear")
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# Scaling of within covariance is: V' 1/S
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scaling = (scaling * V.T[:, :rank].T).T / S[:rank]
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## ----------------------------
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## 3) Between variance scaling
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# Overall mean
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xbar = np.dot(self.priors_, self.means_)
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# Scale weighted centers
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X = np.dot(((np.sqrt((n_samples * self.priors_) * fac)) *
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(means - xbar).T).T, scaling)
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# Centers are living in a space with n_classes-1 dim (maximum)
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# Use svd to find projection in the space spanned by the
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# (n_classes) centers
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_, S, V = linalg.svd(X, full_matrices=0)
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rank = np.sum(S > tol * S[0])
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# compose the scalings
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self.scaling = np.dot(scaling, V.T[:, :rank])
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self.xbar_ = xbar
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# weight vectors / centroids
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self.coef_ = np.dot(self.means_ - self.xbar_, self.scaling)
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self.intercept_ = -0.5 * np.sum(self.coef_ ** 2, axis=1) + \
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np.log(self.priors_)
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self.classes = classes
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return self
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def decision_function(self, X):
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"""
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This function return the decision function values related to each
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class on an array of test vectors X.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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C : array, shape = [n_samples, n_classes]
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"""
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X = np.asanyarray(X)
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# center and scale data
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X = np.dot(X - self.xbar_, self.scaling)
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return np.dot(X, self.coef_.T) + self.intercept_
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def transform(self, X):
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"""
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Project the data so as to maximize class separation (large separation
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between projected class means and small variance within each class).
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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X_new : array, shape = [n_samples, n_components]
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"""
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X = np.asanyarray(X)
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# center and scale data
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X = np.dot(X - self.xbar_, self.scaling)
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n_comp = X.shape[1] if self.n_components is None else self.n_components
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return np.dot(X, self.coef_[:n_comp].T)
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def predict(self, X):
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"""
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This function does classification on an array of test vectors X.
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The predicted class C for each sample in X is returned.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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C : array, shape = [n_samples]
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"""
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d = self.decision_function(X)
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y_pred = self.classes[d.argmax(1)]
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return y_pred
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def predict_proba(self, X):
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"""
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This function return posterior probabilities of classification
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according to each class on an array of test vectors X.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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C : array, shape = [n_samples, n_classes]
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"""
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values = self.decision_function(X)
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# compute the likelihood of the underlying gaussian models
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# up to a multiplicative constant.
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likelihood = np.exp(values - values.max(axis=1)[:, np.newaxis])
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# compute posterior probabilities
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return likelihood / likelihood.sum(axis=1)[:, np.newaxis]
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def predict_log_proba(self, X):
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"""
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This function return posterior log-probabilities of classification
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according to each class on an array of test vectors X.
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Parameters
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----------
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X : array-like, shape = [n_samples, n_features]
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Returns
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-------
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C : array, shape = [n_samples, n_classes]
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"""
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values = self.decision_function(X)
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loglikelihood = (values - values.max(axis=1)[:, np.newaxis])
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normalization = logsum(loglikelihood, axis=1)
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return loglikelihood - normalization[:, np.newaxis]
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