Ejemplo n.º 1
0
 def __init__(self, records, mixtures):
     Model.__init__(self, records)
     self.mixtures = mixtures
     self.mu = np.random.permutation(records)[0:mixtures]
     self.cov = np.ones(
         (mixtures, self.dimensions, self.dimensions)) * np.cov(records.T)
     self.theta = np.ones(mixtures) * (1 / mixtures)
Ejemplo n.º 2
0
    def __init__(self, training_set=None, testing_set=None):
        Model.__init__(self, training_set, testing_set)

        # points that define the target function
        self.point1 = (random.uniform(-1, 1), random.uniform(-1, 1))
        self.point2 = (random.uniform(-1, 1), random.uniform(-1, 1))
        self.weights = [0., 0., 0.]
Ejemplo n.º 3
0
    def __init__(self, training_set=None, testing_set=None, weights=None):
        Model.__init__(self, training_set, testing_set)

        if weights is None:
            self.weights = np.array([[0., 0., 0.]]).T
        else:
            self.weights = weights

        # points that define the target function
        self.point1 = (random.uniform(-1, 1), random.uniform(-1, 1))
        self.point2 = (random.uniform(-1, 1), random.uniform(-1, 1))
Ejemplo n.º 4
0
    def __init__(self, training_set=None, testing_set=None, weights=None):
        Model.__init__(self, training_set, testing_set)

        if weights is None:
            self.weights = np.array([[0., 0., 0.]]).T
        else:
            self.weights = weights

        # points that define the target function
        self.point1 = (random.uniform(-1, 1), random.uniform(-1, 1))
        self.point2 = (random.uniform(-1, 1), random.uniform(-1, 1))
 def __init__(self, training_set=None, testing_set=None):
     Model.__init__(self, training_set, testing_set)
Ejemplo n.º 6
0
 def __init__(self,records):
     Model.__init__(self,records)
     self.mu = np.random.permutation(records)[0]
     self.cov = np.cov(records.T)
     self.cov = np.diag(np.diag(self.cov))
     self.nu = 1000
Ejemplo n.º 7
0
 def __init__(self, records, factors):
     Model.__init__(self, records)
     self.factors = factors
     self.mu = np.mean(records, 0)
     self.cov = np.diag(np.cov(records.T))
     self.phi = np.random.rand(self.dimensions, factors)
 def __init__(self, training_set=None, testing_set=None):
     Model.__init__(self, training_set, testing_set)