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| import pandas as pd # 用于读取数据文件
import tensorflow as tf
import matplotlib.pyplot as plt # 用于画图
import numpy as np
df = pd.read_csv("input.csv", header=None)
train_data = df.values
print(train_data)
train_X = train_data[:, :-1]
train_y = train_data[:, -1:]
feature_num = len(train_X[0])
sample_num = len(train_X)
print("Size of train_X: {}x{}".format(sample_num, feature_num))
print("Size of train_y: {}x{}".format(len(train_y), len(train_y[0])))
X = tf.placeholder(tf.float32)
y = tf.placeholder(tf.float32)
W = tf.Variable(tf.zeros([feature_num, 1]))
b = tf.Variable([-.9])
db = tf.matmul(X, tf.reshape(W, [-1, 1])) + b
hyp = tf.sigmoid(db)
cost0 = y * tf.log(hyp)
cost1 = (1 - y) * tf.log(1 - hyp)
cost = (cost0 + cost1) / -sample_num
loss = tf.reduce_sum(cost)
optimizer = tf.train.GradientDescentOptimizer(0.001)
train = optimizer.minimize(loss)
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)
feed_dict = {X: train_X, y: train_y}
for step in range(1000000):
sess.run(train, {X: train_X, y: train_y})
if step % 10000 == 0:
print(step, sess.run(W).flatten(), sess.run(b).flatten())
# 绘图
w = [0.7672361 , -0.276697 , -0.19542742]
b = 0.09650069
from mpl_toolkits.mplot3d import Axes3D
x1 = train_data[:, 0]
x2 = train_data[:, 1]
x3 = train_data[:, 2]
y = train_data[:, -1:]
fig=plt.figure()
ax=Axes3D(fig)
for x1p, x2p, x3p, yp in zip(x1, x2, x3, y):
if yp == 0:
ax.scatter(x1p, x2p, x3p, c='r')
else:
ax.scatter(x1p, x2p, x3p, c='g')
ax.set_zlabel('Z') # 坐标轴
ax.set_ylabel('Y')
ax.set_xlabel('X')
a = 0.7672361
b = -0.276697
c = -0.19542742
d = 0.09650069
x1 = np.linspace(-1,1,10)
y1 = np.linspace(-1,1,10)
X,Y = np.meshgrid(x1,y1)
Z = (d - a*X - b*Y) / c
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z)
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