本文整理汇总了Python中sklearn.decomposition.IncrementalPCA.get_covariance方法的典型用法代码示例。如果您正苦于以下问题：Python IncrementalPCA.get_covariance方法的具体用法？Python IncrementalPCA.get_covariance怎么用？Python IncrementalPCA.get_covariance使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.decomposition.IncrementalPCA的用法示例。
在下文中一共展示了IncrementalPCA.get_covariance方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: compute_pca
# 需要导入模块: from sklearn.decomposition import IncrementalPCA [as 别名]
# 或者: from sklearn.decomposition.IncrementalPCA import get_covariance [as 别名]
def compute_pca(data_path=os.path.join(BASE_DIR, 'data/memmap/'),
out_path=os.path.join(BASE_DIR, 'data/'),
batch_size=500, image_size=3*300*300):
ipca = IncrementalPCA(n_components=3, batch_size=batch_size)
path = os.path.join(data_path, 'tn_x.dat')
train = np.memmap(path, dtype=theano.config.floatX, mode='r+', shape=(4044,image_size))
n_samples, _ = train.shape
for batch_num, batch in enumerate(gen_batches(n_samples, batch_size)):
X = train[batch,:]
X = np.reshape(X, (X.shape[0], 3, int(image_size/3)))
X = X.transpose(0, 2, 1)
X = np.reshape(X, (reduce(np.multiply, X.shape[:2]), 3))
ipca.partial_fit(X)
path = os.path.join(data_path, 'v_x.dat')
valid = np.memmap(path, dtype=theano.config.floatX, mode='r+', shape=(500,image_size))
n_samples, _ = valid.shape
for batch_num, batch in enumerate(gen_batches(n_samples, batch_size)):
X = valid[batch,:]
X = np.reshape(X, (X.shape[0], 3, int(image_size/3)))
X = X.transpose(0, 2, 1)
X = np.reshape(X, (reduce(np.multiply, X.shape[:2]), 3))
ipca.partial_fit(X)
eigenvalues, eigenvectors = np.linalg.eig(ipca.get_covariance())
eigenvalues.astype('float32').dump(os.path.join(out_path, 'eigenvalues.dat'))
eigenvectors.astype('float32').dump(os.path.join(out_path, 'eigenvectors.dat'))示例2: test_incremental_pca
# 需要导入模块: from sklearn.decomposition import IncrementalPCA [as 别名]
# 或者: from sklearn.decomposition.IncrementalPCA import get_covariance [as 别名]
def test_incremental_pca():
"""Incremental PCA on dense arrays."""
X = iris.data
batch_size = X.shape[0] // 3
ipca = IncrementalPCA(n_components=2, batch_size=batch_size)
pca = PCA(n_components=2)
pca.fit_transform(X)
X_transformed = ipca.fit_transform(X)
np.testing.assert_equal(X_transformed.shape, (X.shape[0], 2))
assert_almost_equal(ipca.explained_variance_ratio_.sum(),
pca.explained_variance_ratio_.sum(), 1)
for n_components in [1, 2, X.shape[1]]:
ipca = IncrementalPCA(n_components, batch_size=batch_size)
ipca.fit(X)
cov = ipca.get_covariance()
precision = ipca.get_precision()
assert_array_almost_equal(np.dot(cov, precision),
np.eye(X.shape[1]))