"""
Author: Victoria
Created on: 2017.9.15 11:45
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
def LDA(X0, X1):
 """
 Get the optimal params of LDA model given training data.
 Input:
 X0: np.array with shape [N1, d]
 X1: np.array with shape [N2, d]
 Return:
 omega: np.array with shape [1, d]. Optimal params of LDA.
 """
 #shape [1, d]
 mean0 = np.mean(X0, axis=0, keepdims=True)
 mean1 = np.mean(X1, axis=0, keepdims=True)
 Sw = (X0-mean0).T.dot(X0-mean0) + (X1-mean1).T.dot(X1-mean1)
 omega = np.linalg.inv(Sw).dot((mean0-mean1).T)
 return omega
if __name__=="__main__":
 #read data from xls
 work_book = pd.read_csv("../data/watermelon_3a.csv", header=None)
 positive_data = work_book.values[work_book.values[:, -1] == 1.0, :]
 negative_data = work_book.values[work_book.values[:, -1] == 0.0, :]
 print (positive_data)
 #LDA
 omega = LDA(negative_data[:, 1:-1], positive_data[:, 1:-1])
 #plot
 plt.plot(positive_data[:, 1], positive_data[:, 2], "bo")
 plt.plot(negative_data[:, 1], negative_data[:, 2], "r+")
 lda_left = 0
 lda_right = -(omega[0]*0.9) / omega[1]
 plt.plot([0, 0.9], [lda_left, lda_right], 'g-')
 plt.xlabel('density')
 plt.ylabel('sugar rate')
 plt.title("LDA")
 plt.show()

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