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)
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.]
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, 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)
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
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)
