""" 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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