def _calcProjectionVector(self, dataX, dataY):
import scipy.linalg
SigmaXX = calcCovarianceMatrix(dataX, dataX)
SigmaYY = calcCovarianceMatrix(dataY, dataY)
SigmaXY = calcCovarianceMatrix(dataX, dataY)
leftMatrix = SigmaXY * SigmaYY.I * SigmaXY.T
rightMatrix = SigmaXX
eigenValues, eigenVectors = scipy.linalg.eig(leftMatrix, rightMatrix)
maxEigenValueIndex = np.where(eigenValues == max(eigenValues))
maxEigenVector = eigenVectors[maxEigenValueIndex]
return maxEigenVector/np.linalg.norm(maxEigenVector)
def _calcProjectionVector(self, dataX, dataY):
import scipy.linalg
SigmaXX = calcCovarianceMatrix(dataX, dataX)
SigmaYY = calcCovarianceMatrix(dataY, dataY)
SigmaXY = calcCovarianceMatrix(dataX, dataY)
leftMatrix = SigmaXY * SigmaYY.I * SigmaXY.T
rightMatrix = SigmaXX
eigenValues, eigenVectors = scipy.linalg.eig(leftMatrix, rightMatrix)
maxEigenValueIndex = np.where(eigenValues == max(eigenValues))
maxEigenVector = eigenVectors[maxEigenValueIndex]
return maxEigenVector/np.linalg.norm(maxEigenVector)
def _removeThirdVariable(self, framesInLabel):
Xti = self.Xt[framesInLabel]
Ytkmi = self.Ytkm[framesInLabel]
Xtkmi = self.Xtkm[framesInLabel]
SigmaXtiXtkmi = calcCovarianceMatrix(Xti, Xtkmi)
SigmaYtkmiXtkmi = calcCovarianceMatrix(Ytkmi, Xtkmi)
SigmaXtkmiXtkmi = calcCovarianceMatrix(Xtkmi, Xtkmi)
#逆行列が計算出来ないことがあるので、正則化する
#詳しくは Yamashita et al. Causal Flow を参照
regularized_eta = 0.00001
AHat = SigmaXtiXtkmi * (SigmaXtkmiXtkmi + regularized_eta * np.matrix(np.identity(SigmaXtkmiXtkmi.shape[0]))).I
BHat = SigmaYtkmiXtkmi * (SigmaXtkmiXtkmi + regularized_eta * np.matrix(np.identity(SigmaXtkmiXtkmi.shape[0]))).I
# AHat = SigmaXtiXtkmi * SigmaXtkmiXtkmi.I
# BHat = SigmaYtkmiXtkmi * SigmaXtkmiXtkmi.I
XtDash = self.Xt - self.Xtkm * AHat.T #TODO 式がちゃんとあっているか確認すべき
YtkmDash = self.Ytkm - self.Xtkm * BHat.T
return XtDash, YtkmDash
def _removeThirdVariable(self, framesInLabel):
Xti = self.Xt[framesInLabel]
Ytkmi = self.Ytkm[framesInLabel]
Xtkmi = self.Xtkm[framesInLabel]
SigmaXtiXtkmi = calcCovarianceMatrix(Xti, Xtkmi)
SigmaYtkmiXtkmi = calcCovarianceMatrix(Ytkmi, Xtkmi)
SigmaXtkmiXtkmi = calcCovarianceMatrix(Xtkmi, Xtkmi)
#逆行列が計算出来ないことがあるので、正則化する
#詳しくは Yamashita et al. Causal Flow を参照
regularized_eta = 0.00001
AHat = SigmaXtiXtkmi * (SigmaXtkmiXtkmi + regularized_eta * np.matrix(np.identity(SigmaXtkmiXtkmi.shape[0]))).I
BHat = SigmaYtkmiXtkmi * (SigmaXtkmiXtkmi + regularized_eta * np.matrix(np.identity(SigmaXtkmiXtkmi.shape[0]))).I
# AHat = SigmaXtiXtkmi * SigmaXtkmiXtkmi.I
# BHat = SigmaYtkmiXtkmi * SigmaXtkmiXtkmi.I
XtDash = self.Xt - self.Xtkm * AHat.T #TODO 式がちゃんとあっているか確認すべき
YtkmDash = self.Ytkm - self.Xtkm * BHat.T
return XtDash, YtkmDash
