def calcRegularizedGrangerCausality(self, pcaContribution, eta_xtkm,
eta_xt, eta_ytkm):
if (len(self.Xt) == 0):
print "サンプルがありません"
return False
pca = PCAApplier(pcaContribution)
import copy
self.Xt = copy.deepcopy(pca.applyPCA(self.Xt))
self.Xtkm = copy.deepcopy(pca.applyPCA(self.Xtkm))
self.Ytkm = copy.deepcopy(pca.applyPCA(self.Ytkm))
# self.Xt = copy.deepcopy(pca.applyKernelPCA(self.Xt))
# self.Xtkm = copy.deepcopy(pca.applyKernelPCA(self.Xtkm))
# self.Ytkm = copy.deepcopy(pca.applyKernelPCA(self.Ytkm))
eigen_values = self.calcEquation21(eta_xtkm, eta_xt, eta_ytkm)
eigen_value = max(eigen_values)
if eigen_value == None:
print "固有値が計算出来ません"
return False
return (np.log2(1 / (1 - eigen_value)) * 0.5).real
minFrames = min(loaderX.getNumFrames(), loaderY.getNumFrames())
loaderX.adjustNumFrames(minFrames)
loaderY.adjustNumFrames(minFrames)
frames_embeddedVecXt = loaderX.generateEmbeddedVec( 1, 0, numDeleteHeadFrames = embeddedTime+delayTime-1)
frames_embeddedVecXtkm = loaderX.generateEmbeddedVec(embeddedTime, delayTime, numSamplingFrames = numSamplingFrames)
frames_embeddedVecYtkm = loaderY.generateEmbeddedVec(embeddedTime, delayTime, numSamplingFrames = numSamplingFrames)
from itertools import izip
import numpy as np
embedded_vector = [np.r_[ np.r_[vector0, vectorX], vectorY] for vector0, vectorX, vectorY in izip(frames_embeddedVecXt, frames_embeddedVecXtkm, frames_embeddedVecYtkm)]
pca_applier_for_classify = PCAApplier(0.9)
frames_axes_pcaedDataMat = pca_applier_for_classify.applyPCA(embedded_vector)
from sklearn.cluster import KMeans
kmeans_model = KMeans(n_clusters=numCluster).fit(frames_axes_pcaedDataMat)
frames_labels = kmeans_model.labels_
import sys, time
import cPickle as pickle
pickle_dic = {"datafileX":datafileX,
"datafileY":datafileY,
# "serial_results":serial_results,
"serial_results":[frames_labels],
"embeddedTime":embeddedTime,
frames_embeddedVecYtkm = loaderY.generateEmbeddedVec(embeddedTime, delayTime, numSamplingFrames=numSamplingFrames)
data = [[np.matrix(embeddedVecXt).T, np.matrix(embeddedVecYtkm).T, np.matrix(embeddedVecXtkm).T]
for i, [embeddedVecXt, embeddedVecYtkm, embeddedVecXtkm] in enumerate(izip(frames_embeddedVecXt, frames_embeddedVecYtkm, frames_embeddedVecXtkm))]
pcaed_data = get_pcaedData(data, 2)
##
frames_positions_x = loaderX.GenerateDataForMovie(embeddedTime + delayTime - 1)
frames_positions_y = loaderY.GenerateDataForMovie(embeddedTime + delayTime - 1)
#############
frames_labels = serial_results[-1]
labels_frames_embeddedVecXt, labels_frames_embeddedVecXtkm, labels_frames_embeddedVecYtkm = SplitData(data, frames_labels)
from PCAApplier import PCAApplier
pcaContribution = 0.99
pca = PCAApplier(pcaContribution)
labels_pcaed_Xt = []
labels_pcaed_Xtkm = []
labels_pcaed_Ytkm = []
for i in range(numCluster):
labels_pcaed_Xt.append(pca.applyPCA(labels_frames_embeddedVecXt[i]))
labels_pcaed_Xtkm.append(pca.applyPCA(labels_frames_embeddedVecXtkm[i]))
labels_pcaed_Ytkm.append(pca.applyPCA(labels_frames_embeddedVecYtkm[i]))
# calc Causalities
from CausalityCalculator import CausalityCalculator
# from SurrogatingTester import SurrogatingTester
# num_try = 100
# tester = SurrogatingTester(num_try, numCluster, labels_frames_embeddedVecXt, labels_frames_embeddedVecXtkm, labels_frames_embeddedVecYtkm)
