我们从Python开源项目中，提取了以下9个代码示例，用于说明如何使用scipy.cov()。
def __init__(self): self.ni = [] self.prop = [] self.mean = [] self.cov =[] self.Q = [] self.L = [] self.classnum = [] # to keep right labels self.tau = 0.0
def learn(self,x,y): ''' Function that learns the GMM with ridge regularizationb from training samples Input: x : the training samples y : the labels Output: the mean, covariance and proportion of each class, as well as the spectral decomposition of the covariance matrix ''' ## Get information from the data C = sp.unique(y).shape[0] #C = int(y.max(0)) # Number of classes n = x.shape[0] # Number of samples d = x.shape[1] # Number of variables eps = sp.finfo(sp.float64).eps ## Initialization self.ni = sp.empty((C,1)) # Vector of number of samples for each class self.prop = sp.empty((C,1)) # Vector of proportion self.mean = sp.empty((C,d)) # Vector of means self.cov = sp.empty((C,d,d)) # Matrix of covariance self.Q = sp.empty((C,d,d)) # Matrix of eigenvectors self.L = sp.empty((C,d)) # Vector of eigenvalues self.classnum = sp.empty(C).astype('uint8') ## Learn the parameter of the model for each class for c,cR in enumerate(sp.unique(y)): j = sp.where(y==(cR))[0] self.classnum[c] = cR # Save the right label self.ni[c] = float(j.size) self.prop[c] = self.ni[c]/n self.mean[c,:] = sp.mean(x[j,:],axis=0) self.cov[c,:,:] = sp.cov(x[j,:],bias=1,rowvar=0) # Normalize by ni to be consistent with the update formulae # Spectral decomposition L,Q = linalg.eigh(self.cov[c,:,:]) idx = L.argsort()[::-1] self.L[c,:] = L[idx] self.Q[c,:,:]=Q[:,idx]
def cluster(self, X): self.fit(X) cluster = [X[sp.argmax(self.responsibility, axis=1) == k] for k in range(self.n_classes)] mean = self.center cov = [sp.cov(c, rowvar=0, ddof=0) for c in cluster] return cluster, mean, cov
def _init_params(self, X): init = self.init n_samples, n_features = X.shape n_components = self.n_components if (init == 'kmeans'): km = Kmeans(n_components) clusters, mean, cov = km.cluster(X) coef = sp.array([c.shape[0] / n_samples for c in clusters]) comps = [multivariate_normal(mean[i], cov[i], allow_singular=True) for i in range(n_components)] elif (init == 'rand'): coef = sp.absolute(sprand.randn(n_components)) coef = coef / coef.sum() means = X[sprand.permutation(n_samples)[0: n_components]] clusters = [[] for i in range(n_components)] for x in X: idx = sp.argmin([spla.norm(x - mean) for mean in means]) clusters[idx].append(x) comps = [] for k in range(n_components): mean = means[k] cov = sp.cov(clusters[k], rowvar=0, ddof=0) comps.append(multivariate_normal(mean, cov, allow_singular=True)) self.coef = coef self.comps = comps
def fit(self, X): cov = sp.cov(X, rowvar=0) eigvals, eigvecs = self._eig_decomposition(cov) self.eigvals = eigvals self.eigvecs = eigvecs self.mean = X.mean(axis=0) return sp.dot(X, eigvecs)
def _maximum_likelihood(self, X): n_samples, n_features = X.shape if X.ndim > 1 else (1, X.shape[0]) n_components = self.n_components # Predict mean mu = X.mean(axis=0) # Predict covariance cov = sp.cov(X, rowvar=0) eigvals, eigvecs = self._eig_decomposition(cov) sigma2 = ((sp.sum(cov.diagonal()) - sp.sum(eigvals.sum())) / (n_features - n_components)) # FIXME: M < D? weight = sp.dot(eigvecs, sp.diag(sp.sqrt(eigvals - sigma2))) M = sp.dot(weight.T, weight) + sigma2 * sp.eye(n_components) inv_M = spla.inv(M) self.eigvals = eigvals self.eigvecs = eigvecs self.predict_mean = mu self.predict_cov = sp.dot(weight, weight.T) + sigma2 * sp.eye(n_features) self.latent_mean = sp.transpose(sp.dot(inv_M, sp.dot(weight.T, X.T - mu[:, sp.newaxis]))) self.latent_cov = sigma2 * inv_M self.sigma2 = sigma2 # FIXME! self.weight = weight self.inv_M = inv_M return self.latent_mean
def fit(self, X): # Constants max_iter = self.max_iter tol = self.tol n_samples, n_features = X.shape n_components = self.n_components # Initialize parameters self._init_params(X) # Initialize responsibility = sp.empty((n_samples, n_components)) log_likelihood = self.log_likelihood(X) for iter in range(max_iter): # E step for n in range(n_samples): for k in range(n_components): responsibility[n][k] = self.pdf(X[n], k) / self.pdf(X[n]) # M step eff = sp.sum(responsibility, axis=0) for k in range(n_components): # Update mean mean = sp.dot(responsibility[:, k], X) / eff[k] # Update covariance cov = sp.zeros((n_features, n_features)) for n in range(n_samples): cov += responsibility[n][k] * sp.outer(X[n] - mean, X[n] - mean) cov /= eff[k] # Update the k component self.comps[k] = multivariate_normal(mean, cov, allow_singular=True) # Update mixture coefficient self.coef[k] = eff[k] / n_samples # Convergent test log_likelihood_new = self.log_likelihood(X) diff = log_likelihood_new - log_likelihood print('GMM: {0:5d}: {1:10.5e} {2:10.5e}'.format( iter, log_likelihood_new, spla.norm(diff) / spla.norm(log_likelihood))) if (spla.norm(diff) < tol * spla.norm(log_likelihood)): break log_likelihood = log_likelihood_new return self
