def prediction():
with tf.variable_scope("sec_lstm", reuse=tf.AUTO_REUSE):
pred, _ = lstm(1) #预测时只输入[1,time_step,input_size]的测试数据
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
saver.restore(sess, 'model_save1\\modle.ckpt') #参数恢复
# I run the code in windows 10,so use 'model_save1\\modle.ckpt'
# if you run it in Linux,please use 'model_save1/modle.ckpt'
# 取训练集最后一行为测试样本。shape=[1,time_step,input_size]
prev_seq = train_x[-1]
predict = []
# 得到之后100个预测结果
for i in range(100):
next_seq = sess.run(pred, feed_dict={X: [prev_seq]})
predict.append(next_seq[-1])
# 每次得到最后一个时间步的预测结果，与之前的数据加在一起，形成新的测试样本
prev_seq = np.vstack((prev_seq[1:], next_seq[-1]))
# 以折线图表示结果
plt.figure()
plt.plot(list(range(len(normalize_data))), normalize_data, color='b')
plt.plot(list(
range(len(normalize_data),
len(normalize_data) + len(predict))),
predict,
color='r')
plt.show()
def prediction(time_step=20):
X = tf.placeholder(tf.float32, shape=[None, time_step, input_size])
mean, std, test_x, test_y = get_test_data(time_step)
with tf.variable_scope("sec_lstm", reuse=tf.AUTO_REUSE):
pred, _ = lstm(X)
saver = tf.train.Saver(tf.global_variables())
with tf.Session() as sess:
# 参数恢复
module_file = tf.train.latest_checkpoint('model_save2')
saver.restore(sess, module_file)
test_predict = []
for step in range(len(test_x) - 1):
prob = sess.run(pred, feed_dict={X: [test_x[step]], keep_prob: 1})
predict = prob.reshape((-1))
test_predict.extend(predict)
test_y = np.array(test_y) * std[7] + mean[7]
test_predict = np.array(test_predict) * std[7] + mean[7]
acc = np.average(
np.abs(test_predict - test_y[:len(test_predict)]) /
test_y[:len(test_predict)]) # 偏差程度
print("The accuracy of this predict:", acc)
# 以折线图表示结果
plt.figure()
plt.plot(
list(range(len(test_predict))),
test_predict,
color='b',
)
plt.plot(list(range(len(test_y))), test_y, color='r')
plt.show()
def plot_results(xlog, ulog):
t = [0.25 * x for x in range(xlog.shape[1])]
plt.figure()
plt.subplot(311)
plt.plot(t, xlog[3, :], label="altitude", color="b")
plt.ylabel('altitude')
plt.subplot(312)
plt.plot(t, xlog[1, :], label="pitch angle", color="r")
plt.ylabel('pitch angle')
plt.subplot(313)
plt.plot(t, ulog[0, :], label="elevator angle", color="g")
plt.ylabel('elevator angle')
plt.show()
from pandas import read_csv
from Example_matplotlib import pyplot
# load dataset
dataset = read_csv('pollution.csv', header=0, index_col=0)
values = dataset.values
# specify columns to plot
groups = [0, 1, 2, 3, 5, 6, 7]
i = 1
# plot each column
pyplot.figure()
for group in groups:
pyplot.subplot(len(groups), 1, i)
pyplot.plot(values[:, group])
pyplot.title(dataset.columns[group], y=0.5, loc='right')
i += 1
pyplot.show()
# Encode and decode images from test set and visualize their reconstruction.
n = 4
canvas_orig = np.empty((28 * n, 28 * n))
canvas_recon = np.empty((28 * n, 28 * n))
for i in range(n):
# MNIST test set
batch_x, _ = mnist.test.next_batch(n)
# Encode and decode the digit image
g = sess.run(decoder_op, feed_dict={X: batch_x})
# Display original images
for j in range(n):
# Draw the original digits
canvas_orig[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \
batch_x[j].reshape([28, 28])
# Display reconstructed images
for j in range(n):
# Draw the reconstructed digits
canvas_recon[i * 28:(i + 1) * 28, j * 28:(j + 1) * 28] = \
g[j].reshape([28, 28])
print("Original Images")
plt.figure(figsize=(n, n))
plt.imshow(canvas_orig, origin="upper", cmap="gray")
plt.show()
print("Reconstructed Images")
plt.figure(figsize=(n, n))
plt.imshow(canvas_recon, origin="upper", cmap="gray")
plt.show()
print('Step %i, Loss: %f' % (i, l))
# Testing
# Generator takes noise as input
noise_input = tf.placeholder(tf.float32, shape=[None, latent_dim])
# Rebuild the decoder to create image from noise
decoder = tf.matmul(noise_input,
weights['decoder_h1']) + biases['decoder_b1']
decoder = tf.nn.tanh(decoder)
decoder = tf.matmul(decoder,
weights['decoder_out']) + biases['decoder_out']
decoder = tf.nn.sigmoid(decoder)
# Building a manifold of generated digits
n = 20
x_axis = np.linspace(-3, 3, n)
y_axis = np.linspace(-3, 3, n)
canvas = np.empty((28 * n, 28 * n))
for i, yi in enumerate(x_axis):
for j, xi in enumerate(y_axis):
z_mu = np.array([[xi, yi]] * batch_size)
x_mean = sess.run(decoder, feed_dict={noise_input: z_mu})
canvas[(n - i - 1) * 28:(n - i) * 28, j * 28:(j + 1) * 28] = \
x_mean[0].reshape(28, 28)
plt.figure(figsize=(8, 10))
Xi, Yi = np.meshgrid(x_axis, y_axis)
plt.imshow(canvas, origin="upper", cmap="gray")
plt.show()
#!usr/bin/env Python
# -*- coding: utf-8 -*-
"""
Time:17/1/1
---------------------------
Question: 读取处理过的文件数据并绘图
---------------------------
"""
import pandas as pd
import Example_matplotlib.pyplot as plt
from Air_Pollution_Forcast_Beijing.util import PROCESS_LEVEL1
pd.options.display.expand_frame_repr = False
dataset = pd.read_csv(PROCESS_LEVEL1, header=0, index_col=0)
# loads the 'pollution.csv' and treat each column as a separate subplot
values = dataset.values
groups = [0, 1, 2, 3, 5, 6, 7]
i = 1
plt.figure()
for group in groups:
plt.subplot(len(groups), 1, i)
plt.plot(values[:, group])
plt.title(dataset.columns[group], y=0.5, loc='right')
i += 1
plt.show()
# print(dataset.head())
# -*-coding:utf-8-*-
# @Time : 2019/6/18 0018 16:13
# @Author :zhuxinquan
# @File : matplotlib_03.py
from mpl_toolkits.basemap import Basemap
import Example_matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['KaiTi'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(20, 10)) # 长和宽
map = Basemap(projection='ortho', lat_0=0, lon_0=0)
map.drawmapboundary(fill_color='aqua')
map.fillcontinents(color='#cc9955', lake_color='aqua')
map.drawcoastlines()
lon = 50.4
lat = 1.4
x, y = map(lon, lat)
plt.text(x,
y,
'哈哈哈哈哈哈哈',
fontsize=12,
fontweight='bold',
ha='left',
va='bottom',
color='r')
# -*-coding:utf-8-*-
# @Time : 2019/6/18 0018 10:58
# @Author :zhuxinquan
# @File : matplotlib_01.py
import Example_matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
plt.figure(1)
# map=Basemap()
map = Basemap(llcrnrlon=70, llcrnrlat=3, urcrnrlon=139,
urcrnrlat=54) # 画中国 经135度2分30秒-东经73度40分，北纬3度52分-北纬53度33分
map.drawcoastlines()
map.drawcountries() # 加海岸线
# map.drawrivers(color='blue', linewidth=0.3) # 加河流
CHN = r"C:\Users\Administrator\Downloads"
map.readshapefile(CHN + r'\gadm36_CHN_shp\gadm36_CHN_1',
'states',
drawbounds=True) # 加省界
# map.readshapefile(CHN+r'\gadm36_TWN_shp\gadm36_TWN_1', 'taiwan', drawbounds=True) # 加台湾
plt.title(r'$World\ Map$', fontsize=24)
plt.show()
def pic_map():
import numpy as np
import pandas as pd
import Example_matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from Example_matplotlib.patches import Polygon
from Example_matplotlib.colors import rgb2hex
from Example_matplotlib.collections import PatchCollection
from Example_matplotlib import pylab
plt.rcParams['font.sans-serif'] = ['KaiTi'] # 指定默认字体
plt.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(20, 10)) # 长和宽
# m= Basemap(llcrnrlon=73, llcrnrlat=18, urcrnrlon=135, urcrnrlat=55) #指定中国的经纬度
m = Basemap(llcrnrlon=77,
llcrnrlat=14,
urcrnrlon=140,
urcrnrlat=51,
projection='lcc',
lat_1=33,
lat_2=45,
lon_0=100) # ‘lcc'将投影方式设置为兰伯特投影
# projection='ortho' # 投影方式设置为正投影——类似地球仪
CHN = r'C:\Users\Administrator\Desktop\MyProject\Test_module\Test_matplotlib\data_base'
m.readshapefile(CHN + r'\gadm36_CHN_shp\gadm36_CHN_1',
'states',
drawbounds=True) # 加省界
# 读取数据
df = pd.read_csv('./data_base/data/chnpop.csv')
df['省名'] = df.省名.str[:2]
df.set_index('省名', inplace=True)
# 把每个省的数据映射到colormap上
statenames = []
colors = {}
patches = []
cmap = plt.cm.YlOrRd # 国旗色红黄色调
vmax = 10**8
vmin = 3 * 10**6
# 处理地图包里的省名
for shapedict in m.states_info:
statename = shapedict['NL_NAME_1']
p = statename.split('|')
if len(p) > 1:
s = p[1]
else:
s = p[0]
s = s[:2]
if s == '黑龍':
s = '黑龙'
statenames.append(s)
pop = df['人口数'][s]
colors[s] = cmap(np.sqrt(
(pop - vmin) / (vmax - vmin)))[:3] # 根据归一化后的人口数映射颜色
# exit()
ax = plt.gca()
for nshape, seg in enumerate(m.states):
color = rgb2hex(colors[statenames[nshape]])
poly = Polygon(seg, facecolor=color, edgecolor=color)
patches.append(poly)
ax.add_patch(poly)
# 图片绘制加上台湾（台湾不可或缺）
m.readshapefile(CHN + '\gadm36_TWN_shp\gadm36_TWN_1',
'taiwan',
drawbounds=True)
for nshape, seg in enumerate(m.taiwan):
poly = Polygon(seg, facecolor='w')
patches.append(poly)
ax.add_patch(poly)
# 添加colorbar 渐变色legend
colors1 = [i[1] for i in colors.values()]
colorVotes = plt.cm.YlOrRd
p = PatchCollection(patches, cmap=colorVotes)
p.set_array(np.array(colors1))
pylab.colorbar(p)
#4266831.094478747, 1662846.3046657
lon = 4097273.638675578
lat = 4008859.232616643
x, y = m(lon, lat)
plt.text(x,
y,
'国都',
fontsize=120,
fontweight='bold',
ha='left',
va='bottom',
color='k')
m.scatter(x, y, s=200, marker='*', facecolors='r', edgecolors='B') # 绘制首都
plt.title(u'祝鑫泉画的World Map ', fontsize=24)
plt.savefig("./data_base/result/Chinese_Map1.png", dpi=100) # 指定分辨率
plt.show()
# -*-coding:utf-8-*-
# @Time : 2019/6/18 0018 13:53
# @Author :zhuxinquan
# @File : matplotlib_pic_China.py
import Example_matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from Example_matplotlib.patches import Polygon
plt.figure(figsize=(16, 8))
m = Basemap(llcrnrlon=77,
llcrnrlat=14,
urcrnrlon=140,
urcrnrlat=51,
projection='lcc',
lat_1=33,
lat_2=45,
lon_0=100)
m.drawcoastlines() # 画上海岸线
m.drawcountries(linewidth=1.5) # 画上国家
# 画上行政区
CHN = r'C:\Users\Administrator\Desktop\MyProject\Test_module\Test_matplotlib\data_base'
m.readshapefile(CHN + r'\gadm36_CHN_shp\gadm36_CHN_1',
'states',
drawbounds=True) # 加省界
# 上色
ax = plt.gca()
for nshape, seg in enumerate(m.states):
# 构造函数原型__init__(self,n_components,perplexity,early_exaggeration,learning_rate,n_iter,
# n_iter_without_progress,min_grad_norm,metric,init,verbose,random_state,
# method,angle)
tsne = TSNE(perplexity=30, n_components=2, init="pca", n_iter=5000)
plot_only = 100
# 执行降维操作
# 函数原型TSNE.fit_transform(Self,X,y)
low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
labels = list()
for i in range(plot_only):
labels.append(reverse_dictionary[i])
# pyplot的figure()函数用于定义画布的大小，这里设为20x20，你也可以尝试其他大小
plt.figure(figsize=(20, 20))
for j, label in enumerate(labels):
x, y = low_dim_embs[j, :]
plt.scatter(x, y)
plt.annotate(label,
xy=(x, y),
xytext=(5, 2),
textcoords="offset points",
ha="right",
va="bottom")
# 以png格式保存图片
plt.savefig("./after_tsne.png")
