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 = []