In [41]:
import numpy as np
from collections import namedtuple
a = 1.332545342543623
print(a)
print(np.round(a, 4))
print(np.float64(a))
print(np.round(np.float64(a), 4))
b = np.round(a, 4)
Rule = namedtuple('Rule', ['multiplier', 'round'])
CONVERTER = {
'open': Rule(1, 4),
'close': Rule(1, 4),
'high': Rule(1, 4),
'low': Rule(1, 4),
'limit_up': Rule(1, 4),
'limit_down': Rule(1, 4),
}
CONVERTER['limit_up'].round
Out[41]:
In [26]:
import numpy as np
from matplotlib import pyplot as plt
x = np.linspace(0, 100, 11)
print(x)
y = x * x + x + 1
print(y)
plt.figure()
plt.title('Single Variable')
plt.xlabel('x-axis') # x轴文本
plt.ylabel('y-axis') # y轴文本
plt.grid(False) # 是否绘制网格线
plt.plot(x, y, 'g-.') # 绘制y=2x图像，颜色green，形式为线条
plt.show() # 展示图像
In [7]:
import numpy as np
a = np.array([[1, 1, 1, 1], [2, 2, 2, 2]])
b = np.array([[1, 1, 1], [2, 2, 2], [3, 3, 3], [4, 4, 4]])
a.dot(b)
Out[7]:
In [26]:
'''
A linear regression learning algorithm example using TensorFlow library.
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
'''
from __future__ import print_function
import tensorflow as tf
import numpy
import matplotlib.pyplot as plt
rng = numpy.random
# Parameters
learning_rate = 0.01
training_epochs = 1000
display_step = 50
# Training Data
# train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
# 7.042,10.791,5.313,7.997,5.654,9.27,3.1])
# train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
# 2.827,3.465,1.65,2.904,2.42,2.94,1.3])
train_X = x = np.linspace(1, 10, 10)
train_Y = x * x * x + x + 1
n_samples = train_X.shape[0]
# tf Graph Input
X = tf.placeholder("float")
Y = tf.placeholder("float")
# Set model weights
W = tf.Variable(rng.randn(), name="weight")
b = tf.Variable(rng.randn(), name="bias")
# Construct a linear model
pred = tf.add(tf.multiply(X, W), b)
# Mean squared error
cost = tf.reduce_sum(tf.pow(pred-Y, 2))/(2*n_samples)
# Gradient descent
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
# Initializing the variables
init = tf.global_variables_initializer()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Fit all training data
for epoch in range(training_epochs):
for (x, y) in zip(train_X, train_Y):
sess.run(optimizer, feed_dict={X: x, Y: y})
# Display logs per epoch step
if (epoch+1) % display_step == 0:
c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
"W=", sess.run(W), "b=", sess.run(b))
print("Optimization Finished!")
training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
# Graphic display
plt.plot(train_X, train_Y, 'ro', label='Original data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()
# Testing example, as requested (Issue #2)
test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
print("Testing... (Mean square loss Comparison)")
testing_cost = sess.run(
tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
feed_dict={X: test_X, Y: test_Y}) # same function as cost above
print("Testing cost=", testing_cost)
print("Absolute mean square loss difference:", abs(
training_cost - testing_cost))
plt.plot(test_X, test_Y, 'bo', label='Testing data')
plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
plt.legend()
plt.show()