def learnModel(self, X, Y):
"""
Learn the weight matrix which matches X and Y.
"""
Parameter.checkClass(X, numpy.ndarray)
Parameter.checkClass(Y, numpy.ndarray)
Parameter.checkInt(X.shape[0], 1, float('inf'))
Parameter.checkInt(X.shape[1], 1, float('inf'))
self.pdcca = PrimalDualCCA(self.kernel, self.tau1, self.tau2)
alpha, V, lmbdas = self.pdcca.learnModel(X, Y)
a = 10**-5
I = numpy.eye(V.shape[0])
VV = numpy.dot(V, V.T) + a * I
self.A = Util.mdot(alpha, V.T, numpy.linalg.inv(VV))
self.X = X
return self.A
class PrimalDualCCARegression(AbstractPredictor):
def __init__(self, kernel, tau1, tau2):
Parameter.checkFloat(tau1, 0.0, float('inf'))
Parameter.checkFloat(tau2, 0.0, float('inf'))
Parameter.checkClass(kernel, AbstractKernel)
self.tau1 = tau1
self.tau2 = tau2
self.kernel = kernel
def setTau1(self, tau1):
Parameter.checkFloat(tau1, 0.0, float('inf'))
self.tau1 = tau1
def getTau1(self):
return self.tau1
def learnModel(self, X, Y):
"""
Learn the weight matrix which matches X and Y.
"""
Parameter.checkClass(X, numpy.ndarray)
Parameter.checkClass(Y, numpy.ndarray)
Parameter.checkInt(X.shape[0], 1, float('inf'))
Parameter.checkInt(X.shape[1], 1, float('inf'))
self.pdcca = PrimalDualCCA(self.kernel, self.tau1, self.tau2)
alpha, V, lmbdas = self.pdcca.learnModel(X, Y)
a = 10**-5
I = numpy.eye(V.shape[0])
VV = numpy.dot(V, V.T) + a*I
self.A = Util.mdot(alpha, V.T, numpy.linalg.inv(VV))
self.X = X
return self.A
def getWeights(self):
return self.A
def predict(self, X):
return numpy.dot(self.kernel.evaluate(X, self.X), self.A)
def __str__(self):
return "PrimalDualCCARegressor: tau1 = " + str(self.tau1)
class PrimalDualCCARegression(AbstractPredictor):
def __init__(self, kernel, tau1, tau2):
Parameter.checkFloat(tau1, 0.0, float('inf'))
Parameter.checkFloat(tau2, 0.0, float('inf'))
Parameter.checkClass(kernel, AbstractKernel)
self.tau1 = tau1
self.tau2 = tau2
self.kernel = kernel
def setTau1(self, tau1):
Parameter.checkFloat(tau1, 0.0, float('inf'))
self.tau1 = tau1
def getTau1(self):
return self.tau1
def learnModel(self, X, Y):
"""
Learn the weight matrix which matches X and Y.
"""
Parameter.checkClass(X, numpy.ndarray)
Parameter.checkClass(Y, numpy.ndarray)
Parameter.checkInt(X.shape[0], 1, float('inf'))
Parameter.checkInt(X.shape[1], 1, float('inf'))
self.pdcca = PrimalDualCCA(self.kernel, self.tau1, self.tau2)
alpha, V, lmbdas = self.pdcca.learnModel(X, Y)
a = 10**-5
I = numpy.eye(V.shape[0])
VV = numpy.dot(V, V.T) + a * I
self.A = Util.mdot(alpha, V.T, numpy.linalg.inv(VV))
self.X = X
return self.A
def getWeights(self):
return self.A
def predict(self, X):
return numpy.dot(self.kernel.evaluate(X, self.X), self.A)
def __str__(self):
return "PrimalDualCCARegressor: tau1 = " + str(self.tau1)
def learnModel(self, X, Y):
"""
Learn the weight matrix which matches X and Y.
"""
Parameter.checkClass(X, numpy.ndarray)
Parameter.checkClass(Y, numpy.ndarray)
Parameter.checkInt(X.shape[0], 1, float('inf'))
Parameter.checkInt(X.shape[1], 1, float('inf'))
self.pdcca = PrimalDualCCA(self.kernel, self.tau1, self.tau2)
alpha, V, lmbdas = self.pdcca.learnModel(X, Y)
a = 10**-5
I = numpy.eye(V.shape[0])
VV = numpy.dot(V, V.T) + a*I
self.A = Util.mdot(alpha, V.T, numpy.linalg.inv(VV))
self.X = X
return self.A
