def main():
#generator = ArtificialDataGenerator([0.33, 0.33, 0.34],
# ["params1", "params2", "params3"])
generator = ArtificialDataGenerator([0.15, 0.25, 0.6],
[["params1", "params2", "params3"],["params2","params1","params3"],["params3","params2","params1"]])
data = generator.generate(300)
EM = PCCAEMalgorithmn(data, 3)
#result=EM.calcSomeStep(100)
result=EM.calcUntilNoChangeClustering()
correct_cluster_labels = generator.get_z()
return result , generator.z , data
from ArtificialDataGenerator import ArtificialDataGenerator
import numpy as np
if __name__ == '__main__':
generator = ArtificialDataGenerator([0.33, 0.33, 0.34],
["params1", "params2", "params3"])
generator.generate(1000)
kmeans_results = generator.clusteringDataWithKmeans(3)
correct_cluster_labels = generator.get_z()
from Plotter import matchClusterLabelsForKmeans
correct_cluster_labels,kmeans_results = matchClusterLabelsForKmeans(correct_cluster_labels, kmeans_results,3)
num_error = np.count_nonzero(kmeans_results - correct_cluster_labels)
print num_error
f = open("/data1/keisuke.kawano/results/generate_model_kmeans.txt", "a")
f.write("%s\n"%num_error)
f.close()
return serial_results
def getParamsDictionary(self):
params_dic = {
"gamma": self.gamma,
"pi": self.pi,
"mu": self.mu,
"Wx": self.Wx,
"C": self.C,
"Psi": self.Psi
}
return params_dic
if __name__ == '__main__':
generator = ArtificialDataGenerator([0.3, 0.5, 0.2],
["params1", "params2", "params3"])
EM = PCCAEMalgorithmn(generator.generate(1000), 3)
kmeans_results = generator.clusteringDataWithKmeans(3)
######### 過去の方法でやるやつ そんなにいらない
# from CausalPatternExtractor import CausalPatternExtractor
# from random import randint
# data = np.array(generator.get_data())
# print data.shape
# y1 = data[:,0:2]
# y2 = data[:,2:5]
# x = data[:,5:9]
# frames_clusterLabels = [randint(0,2) for i,_ in enumerate(x)]
# clusterLabel_frames = []
# for label_i in range(max(frames_clusterLabels)+1):
if(np.array_equal(serial_gamma[-2], serial_gamma[-1])):
return serial_results
def getParamsDictionary(self):
params_dic = {
"gamma":self.gamma,
"pi":self.pi,
"mu":self.mu,
"Wx":self.Wx,
"C":self.C,
"Psi":self.Psi }
return params_dic
if __name__ == '__main__':
generator = ArtificialDataGenerator([0.3, 0.5, 0.2],
["params1", "params2", "params3"])
data = generator.generate(1000)
EM = PCCAEMalgorithmn(data, 3 )
# kmeans_results = generator.clusteringDataWithKmeans(3)
######### 過去の方法でやるやつ そんなにいらない
# from CausalPatternExtractor import CausalPatternExtractor
# from random import randint
# data = np.array(generator.get_data())
# print data.shape
# y1 = data[:,0:2]
# y2 = data[:,2:5]
# x = data[:,5:9]
# frames_clusterLabels = [randint(0,2) for i,_ in enumerate(x)]
# clusterLabel_frames = []
