Python qr_economicの例

コード例 #1

ファイル: plot_lle_digits.py プロジェクト: victormatheus/scikit-learn

import numpy as np
import pylab as pl
from matplotlib import offsetbox
from scikits.learn.utils.fixes import qr_economic
from scikits.learn import manifold, datasets, decomposition, lda

digits = datasets.load_digits(n_class=6)
X = digits.data
y = digits.target
n_samples, n_features = X.shape

# ----------------------------------------------------------------------
# Random 2D projection using a random unitary matrix
print "Computing random projection"
rng = np.random.RandomState(42)
Q, _ = qr_economic(rng.normal(size=(n_features, 2)))
X_projected = np.dot(Q.T, X.T).T


# ----------------------------------------------------------------------
# Projection on to the first 2 principal components

print "Computing PCA projection"
X_pca = decomposition.RandomizedPCA(n_components=2).fit_transform(X)

# ----------------------------------------------------------------------
# Projection on to the first 2 linear discriminant components

print "Computing LDA projection"
X2 = X.copy()
X2.flat[:: X.shape[1] + 1] += 0.01  # Make X invertible

コード例 #2

ファイル: plot_lle_digits.py プロジェクト: shillenm/scikit-learn

import numpy as np
import pylab as pl
from matplotlib import offsetbox
from scikits.learn.utils.fixes import qr_economic
from scikits.learn import manifold, datasets, decomposition, lda

digits = datasets.load_digits(n_class=6)
X = digits.data
y = digits.target
n_samples, n_features = X.shape

#----------------------------------------------------------------------
# Random 2D projection using a random unitary matrix
print "Computing random projection"
rng = np.random.RandomState(42)
Q, _ = qr_economic(rng.normal(size=(n_features, 2)))
X_projected = np.dot(Q.T, X.T).T

#----------------------------------------------------------------------
# Projection on to the first 2 principal components

print "Computing PCA projection"
X_pca = decomposition.RandomizedPCA(n_components=2).fit_transform(X)

#----------------------------------------------------------------------
# Projection on to the first 2 linear discriminant components

print "Computing LDA projection"
X2 = X.copy()
X2.flat[::X.shape[1] + 1] += 0.01  # Make X invertible
X_lda = lda.LDA(n_components=2).fit_transform(X2, y)