def __init__(self, fm_x, fm_y_1, fm_y_2, fm_y, dim_x, dim_y_1, dim_y_2, dim_y):
super(QMFindingsClassifier, self).__init__()
#print("Empieza el init")
self.fm_x = fm_x
self.fm_y_1 = fm_y_1
self.fm_y_2 = fm_y_2
self.fm_y = fm_y
self.qm1 = layers.QMeasureClassif(dim_x=dim_x, dim_y=dim_y_1)
self.qm2 = layers.QMeasureClassif(dim_x=dim_x, dim_y=dim_y_2)
self.qm = layers.QMeasureClassif(dim_x=4, dim_y=dim_y)
self.dm2dist_1 = layers.DensityMatrix2Dist()
self.dm2dist = layers.DensityMatrix2Dist()
self.cp1 = layers.CrossProduct()
self.cp2 = layers.CrossProduct()
self.cp3 = layers.CrossProduct()
self.cp4 = layers.CrossProduct()
self.cp5 = layers.CrossProduct()
self.cp6 = layers.CrossProduct()
self.cp7 = layers.CrossProduct()
self.num_samples = tf.Variable(
initial_value=0.,
trainable=False
)
def __init__(self, fm_x, fm_y, dim_x, dim_y):
super(QMRegressor, self).__init__()
self.fm_x = fm_x
self.fm_y = fm_y
self.qm = layers.QMeasureClassif(dim_x=dim_x, dim_y=dim_y)
self.dmregress = layers.DensityMatrixRegression()
self.cp1 = layers.CrossProduct()
self.cp2 = layers.CrossProduct()
self.num_samples = tf.Variable(initial_value=0., trainable=False)
def __init__(self, fm_x, fm_y, dim_x, dim_y):
#print("**********init****************")
super(QMClassifier, self).__init__()
self.fm_x = fm_x
self.fm_y = fm_y
self.qm = layers.QMeasureClassif(dim_x=dim_x, dim_y=dim_y)
self.dm2dist = layers.DensityMatrix2Dist()
self.cp1 = layers.CrossProduct()
self.cp2 = layers.CrossProduct()
self.num_samples = tf.Variable(
initial_value=0.,
trainable=False
)