def _transform(self, data, X):
logger.info("Transforming data")
loadings = self.model.loadings[:self.n_components]
loadings = DenseMatrix(X.numCols(), self.n_components,
loadings.flatten())
X = X.multiply(loadings)
data = join(data, X, self.spark)
del X
return data
def fourier(X: RowMatrix, n_features, seed=23, gamma=1):
p = X.numCols()
random_state = numpy.random.RandomState(seed)
w = numpy.sqrt(2 * gamma) * random_state.normal(size=(p, n_features))
w = DenseMatrix(p, n_features, w.flatten(), isTransposed=True)
b = random_state.uniform(0, 2 * numpy.pi, size=n_features)
Y = fourier_transform(X, w, b)
return Y, w, b
def _transform(self, data):
logger.info("Transforming data")
W = self.model.loadings[:, :self.n_components]
W = DenseMatrix(numRows=W.shape[0],
numCols=W.shape[1],
isTransposed=True,
values=W.flatten())
X = self._row_matrix(data).multiply(W)
data = join(data, X, self.spark)
del X
return data
def _whiten(self, X):
s, v, _ = svd(X, X.numCols())
K = (v.T / s)[:, :self.n_components]
S = K * scipy.sqrt(X.numRows())
S = DenseMatrix(S.shape[0], S.shape[1], S.flatten(), True)
return X.multiply(S), K