def test_pcca(self):
rng = np.random.RandomState(1)
X = rng.randn(10, 20)
pairs = [(i, i + 1) for i in range(5)] + [(i, i + 5) for i in range(5)]
labels = np.asarray([1] * 5 + [-1] * 5)
y = np.hstack([pairs, labels[:, np.newaxis]])
callback = dml.CrossValCallback(X, y) if self.callback else None
ml = dml.PCCA(3, callback=callback, verbose=self.verbose)
ml.fit(X, y)
assert (ml.score(X, y) == 1.0)
def test_pcca(self):
rng = np.random.RandomState(1)
#X = rng.randn(10, 20)
#pairs = [(i, i + 1) for i in range(5)] + [(i, i + 5) for i in range(5)]
#labels = np.asarray([1] * 5 + [-1] * 5)
#y = np.hstack([pairs, labels[:, np.newaxis]])
X = np.loadtxt('x.out')
C = np.loadtxt('c.out')
y = np.loadtxt('y.out')
y = np.hstack([C, y[:, np.newaxis]])
#print X,pairs,labels,y
callback = dml.CrossValCallback(X, y) if self.callback else None
ml = dml.PCCA(40, callback=callback, verbose=self.verbose)
ml.fit(X, y)
print ml.coefs_.shape
np.savetxt('projectionMatrix.out', np.asarray(ml.coefs_))
import dml
import unittest
import numpy as np
import csv
X = np.loadtxt('x.out')
C = np.loadtxt('c.out')
y = np.loadtxt('y.out')
y = np.hstack([C, y[:, np.newaxis]])
callback = dml.CrossValCallback(X, y)
ml = dml.PCCA(40, callback=None,
alpha=0,
kernel=True, verbose=False)
ml.fit(X, y)
print ml.coefs_.shape
np.savetxt('projectionMatrix.out', np.asarray(ml.coefs_))