def resetparams(self, parameters):
try:
utils.update_dictionary_items(self.params,parameters)
except AttributeError:
# Variable self.params does not exist, so not updated
# Create an empty set of params for future reference
self.params = {}
def reset(self, params):
""" Can pass parameters to reset with new parameters """
try:
utils.update_dictionary_items(self.params,params)
except AttributeError:
# Variable self.params does not exist, so not updated
# Create an empty set of params for future reference
self.params = {}
def resetparams(self, parameters):
""" Can pass parameters to reset with new parameters """
self.weights = None
try:
utils.update_dictionary_items(self.params,parameters)
except AttributeError:
# Variable self.params does not exist, so not updated
# Create an empty set of params for future reference
self.params = {}
def __init__(self, parameters={}):
""" Params can contain any useful parameters for the algorithm """
# red_class_bias is an additional bias we place in favour of the red fighter,
# where 1 means no bias, 2 means we will only choose blue if it is twice as likely as red,
# 0.5 means that we will only choose red is it is twice as likely as blue, and so on
self.params = utils.update_dictionary_items({'red_class_bias': 1},
parameters)
def __init__(self, parameters={}):
"""Website References:
https://analyticsindiamag.com/understanding-the-basics-of-svm-with-example-and-python-implementation/
https://towardsdatascience.com/support-vector-machine-python-example-d67d9b63f1c8
https://www.kdnuggets.com/2016/06/select-support-vector-machine-kernels.html
https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
"""
self.params = utils.update_dictionary_items({'kernel': 'linear'},
parameters)
if self.params['kernel'] == 'poly':
self.classifier = SVC(kernel=self.params['kernel'],
gamma='scale',
degree=self.params['degree'])
else:
self.classifier = SVC(kernel=self.params['kernel'], gamma='scale')
def __init__(self, parameters={}):
self.params = utils.update_dictionary_items(
{
'nh': 4,
'transfer': 'sigmoid',
'stepsize': 0.001,
'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
