我们从Python开源项目中，提取了以下5个代码示例，用于说明如何使用sklearn.utils.extmath.squared_norm()。
def temp_log_loss(w, X, Y, alpha): n_classes = Y.shape[1] w = w.reshape(n_classes, -1) intercept = w[:, -1] w = w[:, :-1] z = safe_sparse_dot(X, w.T) + intercept denom = expit(z) #print denom #print denom.sum() denom = denom.sum(axis=1).reshape((denom.shape[0], -1)) #print denom p = log_logistic(z) loss = - (Y * p).sum() loss += np.log(denom).sum() loss += 0.5 * alpha * squared_norm(w) return loss
def logistic_loss(w, X, Y, alpha): """ Implementation of the logistic loss function when Y is a probability distribution. loss = -SUM_i SUM_k y_ik * log(P[yi == k]) + alpha * ||w||^2 """ n_classes = Y.shape[1] n_features = X.shape[1] intercept = 0 if n_classes > 2: fit_intercept = w.size == (n_classes * (n_features + 1)) w = w.reshape(n_classes, -1) if fit_intercept: intercept = w[:, -1] w = w[:, :-1] else: fit_intercept = w.size == (n_features + 1) if fit_intercept: intercept = w[-1] w = w[:-1] z = safe_sparse_dot(X, w.T) + intercept if n_classes == 2: # in the binary case, simply compute the logistic function p = np.vstack([log_logistic(-z), log_logistic(z)]).T else: # compute the logistic function for each class and normalize denom = expit(z) denom = denom.sum(axis=1).reshape((denom.shape[0], -1)) p = log_logistic(z) loss = - (Y * p).sum() loss += np.log(denom).sum() # Y.sum() = 1 loss += 0.5 * alpha * squared_norm(w) return loss loss = - (Y * p).sum() + 0.5 * alpha * squared_norm(w) return loss
def test_norm_squared_norm(): X = np.random.RandomState(42).randn(50, 63) X *= 100 # check stability X += 200 assert_almost_equal(np.linalg.norm(X.ravel()), norm(X)) assert_almost_equal(norm(X) ** 2, squared_norm(X), decimal=6) assert_almost_equal(np.linalg.norm(X), np.sqrt(squared_norm(X)), decimal=6)
def norm(x): """Dot product-based Euclidean norm implementation See: http://fseoane.net/blog/2011/computing-the-vector-norm/ """ return math.sqrt(squared_norm(x))
def _kmeans_single_lloyd(X, n_clusters, max_iter=300, init='k-means||', verbose=False, x_squared_norms=None, random_state=None, tol=1e-4, precompute_distances=True, oversampling_factor=2, init_max_iter=None): centers = k_init(X, n_clusters, init=init, oversampling_factor=oversampling_factor, random_state=random_state, max_iter=init_max_iter) dt = X.dtype P = X.shape[1] for i in range(max_iter): t0 = tic() labels, distances = pairwise_distances_argmin_min( X, centers, metric='euclidean', metric_kwargs={"squared": True} ) labels = labels.astype(np.int32) # distances is always float64, but we need it to match X.dtype # for centers_dense, but remain float64 for inertia r = da.atop(_centers_dense, 'ij', X, 'ij', labels, 'i', n_clusters, None, distances.astype(X.dtype), 'i', adjust_chunks={"i": n_clusters, "j": P}, dtype=X.dtype) new_centers = da.from_delayed( sum(r.to_delayed().flatten()), (n_clusters, P), X.dtype ) counts = da.bincount(labels, minlength=n_clusters) # Require at least one per bucket, to avoid division by 0. counts = da.maximum(counts, 1) new_centers = new_centers / counts[:, None] new_centers, = compute(new_centers) # Convergence check shift = squared_norm(centers - new_centers) t1 = tic() logger.info("Lloyd loop %2d. Shift: %0.4f [%.2f s]", i, shift, t1 - t0) if shift < tol: break centers = new_centers if shift > 1e-7: labels, distances = pairwise_distances_argmin_min(X, centers) labels = labels.astype(np.int32) inertia = distances.sum() centers = centers.astype(dt) return labels, inertia, centers, i + 1
