def test():
numSamples = 15000
meanVector = np.array([1., -1., 0.])
covMatrix = np.array([[2., 0.1, 0.], [0.1, 1.5, -0.1], [0., -0.1, 0.01]])
sampler = CorrelatedGaussianSampler(meanVector=meanVector,
covMatrix=covMatrix)
sampledVectors = []
for i in range(numSamples):
sampledVector = sampler.sampleOneVector()
sampledVectors.append(sampledVector)
# find the mean vector
print vecMean(sampledVectors)
# find the covariance matrix
print covMat(sampledVectors)
def test():
numSamples = 15000
meanVector = np.array([1., -1., 0.])
covMatrix = np.array([[2., 0.1, 0.], [0.1, 1.5, -0.1], [0., -0.1, 0.01]])
sampler = CorrelatedGaussianSampler(meanVector=meanVector,
covMatrix=covMatrix)
sampledVectors = []
for i in range(numSamples):
sampledVector = sampler.sampleOneVector()
sampledVectors.append(sampledVector)
# find the mean vector
print vecMean(sampledVectors)
# find the covariance matrix
print covMat(sampledVectors)
def importVectors(self, vectors):
self.trainVectors = vectors
self.covMatrix = covMat(vectors)
self.vecMean = vecMean(vectors)
# PCA
u, s, v = np.linalg.svd(self.covMatrix)
self.diagCovVal = s
self.U = v
self.invU = np.linalg.inv(v)
def importVectors(self, vectors):
self.trainVectors = vectors
self.covMatrix = covMat(vectors)
self.vecMean = vecMean(vectors)
# PCA
u, s, v = np.linalg.svd(self.covMatrix)
self.diagCovVal = s
self.U = v
self.invU = np.linalg.inv(v)
def testrun():
#meanVector = np.array([1., -1.])
#covMatrix = np.array([[1., 0], [0, 2.]])
meanVector = np.array([1., -1., 0.])
covMatrix = np.array([[2., 0.1, 0.], [0.1, 1.5, -0.1], [0., -0.1, 0.01]])
ndim = len(meanVector)
f = partial(unnormalizedMultivariateGaussianDist,
meanVec = meanVector,
covMat = covMatrix)
# for testing and comparison purpose
invCovMat = np.linalg.inv(covMatrix)
f = lambda vec: np.exp(-0.5*np.matrix(vec-meanVector)*np.matrix(invCovMat)*np.transpose(np.matrix(vec-meanVector)))
chain = MarkovChainByGibbsSampling(f, 15000, ndim)
print vecMean(chain)
print covMat(chain)
