def __init__(self, number_actions, number_sources, number_destinations, number_states,
number_hidden_nodes_per_layer, activation_function_type, exploration_type, epsilon, alpha, gamma, beta,
learning_rate, learning_method):
# Create an instance of the Q_Learning class.
self.learn = l.Learn(number_actions, number_sources, number_destinations, number_states,
number_hidden_nodes_per_layer, activation_function_type, exploration_type, epsilon, alpha,
gamma, beta, learning_rate)
self.learning_method = learning_method
self.number_actions = number_actions
import criterion
import parse
import learn
import rank
if __name__ == "__main__":
X = parse.Normalize(
'/home/agiachris/CIBCData'
) # Return Dicitonary with States as Keys and databases and values
stored_data = []
for key in X.keys(): # Iterate over four datasets
print(key)
currData = X[key]
#currData = currData[:100] # Training spliced datasets
stored_data += currData
extData = parse.Extract(
currData) # Extract required data (doctor type, price)
#n = criterion.Number(extData) # Obtain optimal number of Gaussian Components
if key == 'NY':
score_records = learn.Learn(extData, 50,
key) # Cluster, plot, and store
else:
score_records = np.r_[score_records, (learn.Learn(
extData, 50, key))] # Cluster, plot, and store
sorted_data = rank.Sort(
score_records,
stored_data) # Rank dataset according to probability score
rank.File1(sorted_data) # Create csv file1
rank.File2(sorted_data) # Create cvs file2
import learn
l = learn.Learn("This is a learning program")
print(l.share())
def learn_structure(D, Sc, dist = 'multinomial'): S = learn.Learn(D, Sc, 2, dist) spn.save(S, 'spn.net') return S
import learn
import neuralnetwork
import os
import re
learn_path = "./new_train"
dictionary = learn.Learn(learn_path)
condition = True if True else False
amount_of_internal_layers = 1
input_len = 5
output_len = 2
important_dict = []
for i in dictionary.common:
if dictionary.common[i] > 500:
important_dict.append(i)
print(len(important_dict))
with open("words", "w") as f:
f.write(" ".join(important_dict))