import numpy as np
import dataset_util as DS
import NaiveBayes as NB
g_K_param = 100
if __name__ == '__main__':
print("\t\t\t============ Chap4 Naive Bayes ============")
x, y = DS.load_dataset(r'.\dataset.dat')
## convert y to label
y = np.array(list(map(lambda y1: DS.g_Labels.index(y1), y)))
yy = np.reshape(y, (-1, 1))
du = DS.DataUtil(x)
xx = du.discrete_variant_EqtWidth(x, g_K_param)
model = NB.Naive_Bayes(g_K_param)
model.train(xx, yy)
x_t = DS.load_test_ds(r'.\testds.dat')
xx_t = du.discrete_variant_EqtWidth(x_t)
y_t, y_t_p = model(xx_t)
yy_t = list(DS.g_Labels[int(y1)] for y1 in y_t)
print(">>>>Test DATA is: >>>>")
print(x_t)
print(">>>>Navie Bayes give the answer is :>>>>")
print(yy_t)
print(">>>>Navie Bayes answer possibility is :>>>>")
print(y_t_p)
pass
import NaiveBayes
#from pyspark.mllib.classification import NaiveBayes
#from pyspark.mllib.linalg import Vectors
#from pyspark.mllib.regression import LabeledPoint
#from pyspark.mllib.feature import HashingTF
#from pyspark.mllib.feature import IDF
from pyspark import SparkConf, SparkContext
from pyspark.sql import SparkSession
import os
# conf = SparkConf().setAppName("appName").setMaster("local")
# conf.set("spark.executor.memory", "2g")
# sc = SparkContext(conf=conf)
# spark = SparkSession(sc)
classifier = NaiveBayes.Naive_Bayes()
model = classifier.train()
#
# sentence=[]
# sentence.append("I really hate rain! It makes me feel sick!")
# sentence.append("Sunny day! Feeling warm and pleased!")
#
# for i in sentence:
# print(i, "rediction: ", model.predict(i))
