def __init__(self, parameters={}): self.params = utils.update_dictionary_items( { 'regwgt': 0.01, 'features': range(385), 'tolerance': 10e-4, }, parameters)
def __init__(self, parameters = {}): # Default parameters, any of which can be overwritten by values passed to params #self.params = utils.update_dictionary_items({'regwgt': 0.5}, parameters) self.params = utils.update_dictionary_items({ 'regwgt': 0.5, # l2 regularizer 'features': [1,2,3,4,5], }, parameters)
def __init__(self, parameters={}): self.params = utils.update_dictionary_items( { 'stepsize': 0.01, 'epochs': 1000 }, parameters) self.weights = None
def __init__(self, parameters={}): # Default parameters, any of which can be overwritten by values passed to params self.params = utils.update_dictionary_items( { 'regwgt': 0.01, 'features': range(385), }, parameters)
def __init__(self, parameters={}): self.params = utils.update_dictionary_items( { 'regwgt': 0.01, 'stepsize': 0.01, 'tolerance': 10e-4, 'maxiteration': 1000 }, parameters)
def __init__(self, parameters={}): self.params = utils.update_dictionary_items( { 'regwgt': 0.0, 'features': [1, 2, 3, 4, 5], #'features': list(range(1, 385)), }, parameters)
def __init__(self, parameters = {}, iterations = 1000000, tolerance = 10e-4): self.params = utils.update_dictionary_items({ 'iterations': 1000000, 'tolerance': 0.001, }, parameters) self.iterations = iterations self.tolerance = tolerance
def __init__(self, parameters = {},step_size = 0.01, epochs = 1000): self.params = utils.update_dictionary_items({ 'step_size': 0.01, 'epochs': 1000, }, parameters) self.step_size = step_size self.epochs = epochs
def __init__(self, parameters={}): self.params = utils.update_dictionary_items( { 'stepsize': 0.01, 'epochs': 100, 'centers': 10, 'kernel': 'linear' }, parameters) self.weights = None
def __init__(self,parameters = {}, learning_rate = 0.001, iterations = 1000, lamda = 0.0005, tolerance = 10e-4): self.params = utils.update_dictionary_items({ 'learning_rate': 0.001, 'lamda': 0.0005, }, parameters) self.learning_rate = learning_rate self.iterations = iterations self.lamda = lamda self.tolerance = tolerance
def __init__(self, parameters={}): self.params = utils.update_dictionary_items( { 'regwgt': 0.01, 'features': range(385), "epochs": 1000, "stepsize": 0.01, }, parameters) self.noofruns = 5 self.error = np.zeros(1000)
def __init__(self, parameters={}): # Default parameters, any of which can be overwritten by values passed to params self.params = utils.update_dictionary_items( { 'regwgt': 0.01, 'features': [1, 2, 3, 4, 5], 'step_size': 0.01 }, parameters) self.weights = None
def __init__(self, parameters={}): self.params = utils.update_dictionary_items({ 'nh': 4, 'transfer': 'sigmoid', 'stepsize': 0.01, 'epochs': 10, }, parameters) if self.params['transfer'] is 'sigmoid': self.transfer = utils.sigmoid self.dtransfer = utils.dsigmoid else: # For now, only allowing sigmoid transfer raise Exception('NeuralNet -> can only handle sigmoid transfer, must set option transfer to string sigmoid') self.wi = None self.wo = None
def __init__(self, parameters={}): # Default parameters, any of which can be overwritten by values passed to params self.params = utils.update_dictionary_items({'regwgt': 0.5}, parameters) self.weights = None
def __init__(self, parameters = {}): self.params = utils.update_dictionary_items({ 'regwgt': 0.5, # l2 regularizer 'features': [1,2,3,4,5], }, parameters)
def __init__(self, parameters={}): self.params = utils.update_dictionary_items({ "iteration": 1000, }, parameters)
def __init__(self, parameters={}): # Default parameters, any of which can be overwritten by values passed to params self.params = utils.update_dictionary_items({'tau': 0.7}, parameters)
def __init__(self, parameters={}): """ Params can contain any useful parameters for the algorithm """ # Assumes that a bias unit has been added to feature vector as the last feature # If usecolumnones is False, it ignores this last feature self.params = utils.update_dictionary_items({'usecolumnones': False}, parameters)