python_matplotlib

import matplotlib.pyplot as plt
plt.subplot(211)
plt.plot([1,2,3], label="test1")
plt.plot([3,2,1], label="test2")
# Place a legend above this legend, expanding
#itself to fully use the given bounding box.
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3, ncol=2, mode="expand",
borderaxespad=0.)
plt.subplot(223)
plt.plot([1,2,3], label="test1")
plt.plot([3,2,1], label="test2")
# Place a legend to the right of this smaller
# figure.
plt.legend(bbox_to_anchor=(1.05, 1), loc=2,
borderaxespad=0.)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
# Make some fake data.
a = b = np.arange(0,3, 0.02)
c = np.exp(a)
d = c[::-1]
#print c,d
# Create plots with pre-defined labels.
plt.plot(a, c, 'g--', label='Model length')
plt.plot(a, d, 'b:', label='Data length')
plt.plot(a, c+d, 'k', label='Total message
1865d
length')
legend = plt.legend(loc='upper center',
shadow=True, fontsize='x-large')
# Put a nicer background color on the legend.
legend.get_frame().set_facecolor('#00FFCC')

plt.show()

import numpy as np
import matplotlib.pyplot as plt
x1 = np.linspace(0.0, 5.0)

x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)
plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'yo-')
plt.title('A tale of 2 subplots')
plt.ylabel('Damped oscillation')
plt.subplot(2, 1, 2)
plt.plot(x2, y2, 'r.-')
plt.xlabel('time (s)')
plt.ylabel('Undamped')

plt.show()

import matplotlib.pyplot as plt
import numpy as np

plt.ion()

x = np.arange(10)

fig = plt.figure()
ax = plt.subplot(111)

for i in xrange(5):
ax.plot(x, i * x, label='$y = %ix$'%i)

# Put a legend to the right of the current axis
leg = ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))

plt.draw()

# Get the ax dimensions.
box = ax.get_position()
xlocs = (box.x0,box.x1)
ylocs = (box.y0,box.y1)

# Get the figure size in inches and the dpi.
w, h = fig.get_size_inches()
dpi = fig.get_dpi()

# Get the legend size, calculate new window width and change the figure size.
legWidth = leg.get_window_extent().width
winWidthNew = w*dpi+legWidth
fig.set_size_inches(winWidthNew/dpi,h)

# Adjust the window size to fit the figure.
mgr = plt.get_current_fig_manager()
mgr.window.wm_geometry("%ix%i"%(winWidthNew,mgr.window.winfo_height()))

# Rescale the ax to keep its original size.
factor = w*dpi/winWidthNew
x0 = xlocs[0]*factor
x1 = xlocs[1]*factor
width = box.width*factor
ax.set_position([x0,ylocs[0],x1-x0,ylocs[1]-ylocs[0]])

plt.draw()

# coding: utf-8

# In[8]:

def fab(n):
n1=1
n2=1
n3=1
if n<2:
print ('输入有误！')
return -1
while n-2>0:
n3=n2+n1
n1=n2
n2=n3
n-=1
return n3
result=fab(20)
if result!=-1:
print "总共有%d对小兔崽子诞生！" , result

# In[ ]:

print 'this is a good day!'

# In[2]:

def f():
print 'is a good day!'

f()

# In[6]:

from os import path
from scipy.misc import imread
import matplotlib.pyplot as plt

from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator

# 获取当前文件路径
# __file__ 为当前文件, 在ide中运行此行会报错,可改为
d = path.dirname('.')
#d = path.dirname(__file__)

# 读取文本 alice.txt 在包文件的example目录下
#内容为
"""
Project Gutenberg's Alice's Adventures in Wonderland, by Lewis Carroll

This eBook is for the use of anyone anywhere at no cost and with
almost no restrictions whatsoever.  You may copy it, give it away or
re-use it under the terms of the Project Gutenberg License included
with this eBook or online at www.gutenberg.org
"""
text = open(path.join(d, 'alice.txt')).read()

# read the mask / color image
# taken from http://jirkavinse.deviantart.com/art/quot-Real-Life-quot-Alice-282261010 # 设置背景图片
alice_coloring = imread(path.join(d, "aaa.jpg"))

wc = WordCloud(background_color="white", #背景颜色max_words=2000,# 词云显示的最大词数
mask=alice_coloring,#设置背景图片
stopwords=STOPWORDS.add("said"),
max_font_size=40, #字体最大值
random_state=42)
# 生成词云, 可以用generate输入全部文本(中文不好分词),也可以我们计算好词频后使用generate_from_frequencies函数
wc.generate(text)
# wc.generate_from_frequencies(txt_freq)
# txt_freq例子为[('词a', 100),('词b', 90),('词c', 80)]
# 从背景图片生成颜色值
image_colors = ImageColorGenerator(alice_coloring)

# 以下代码显示图片
plt.imshow(wc)
plt.axis("off")
# 绘制词云
plt.figure()
# recolor wordcloud and show
# we could also give color_func=image_colors directly in the constructor
plt.imshow(wc.recolor(color_func=image_colors))
plt.axis("off")
# 绘制背景图片为颜色的图片
plt.figure()
plt.imshow(alice_coloring, cmap=plt.cm.gray)
plt.axis("off")
plt.show()
# 保存图片
wc.to_file(path.join(d, "名称.jpg"))

# In[19]:

""""
Masked wordcloud
================
Using a mask you can generate wordclouds in arbitrary shapes.
"""

from os import path
from PIL import Image
import numpy as np
import matplotlib.pyplot as plt

from wordcloud import WordCloud, STOPWORDS

d = path.dirname('.')

# Read the whole text.
text = open(path.join(d, 'alice.txt')).read()

# read the mask image
# taken from
# http://www.stencilry.org/stencils/movies/alice%20in%20wonderland/255fk.jpg alice_mask = np.array(Image.open(path.join(d, "NSSG.jpg")))

wc = WordCloud(background_color="white", max_words=2000, mask=alice_mask,
stopwords=STOPWORDS.add("said"))
# generate word cloud
wc.generate(text)

# store to file
wc.to_file(path.join(d, "xiaorouhu.png"))

# show
plt.imshow(wc)
plt.axis("off")
plt.figure()
plt.imshow(alice_mask, cmap=plt.cm.gray)
plt.axis("off")
plt.show()

# In[17]:

"""
Using custom colors
====================
Using the recolor method and custom coloring functions.
"""

import numpy as np
from PIL import Image
from os import path
import matplotlib.pyplot as plt
import random

from wordcloud import WordCloud, STOPWORDS

def grey_color_func(word, font_size, position, orientation, random_state=None, **kwargs):
return "hsl(0, 0%%, %d%%)" % random.randint(60, 100)

d = path.dirname('.')

# read the mask image
# taken from
# http://www.stencilry.org/stencils/movies/star%20wars/storm-trooper.gif mask = np.array(Image.open(path.join(d, "NSSG.jpg")))

# movie script of "a new hope"
# http://www.imsdb.com/scripts/Star-Wars-A-New-Hope.html # May the lawyers deem this fair use.
text = open("alice.txt").read()

# preprocessing the text a little bit
text = text.replace("HAN", "Han")
text = text.replace("LUKE'S", "Luke")

# adding movie script specific stopwords
stopwords = STOPWORDS.copy()
stopwords.add("int")
stopwords.add("ext")

wc = WordCloud(max_words=1000, mask=mask, stopwords=stopwords, margin=10,
random_state=1).generate(text)
# store default colored image
default_colors = wc.to_array()
plt.title("Custom colors")
plt.imshow(wc.recolor(color_func=grey_color_func, random_state=3))
wc.to_file("a_new_hope.png")
plt.axis("off")
plt.figure()
plt.title("Default colors")
plt.imshow(default_colors)
plt.axis("off")
plt.show()

# In[16]:

#汉语专用的模块

"""
Minimal Example
===============
Generating a square wordcloud from the US constitution using default arguments.
"""

from os import path
from wordcloud import WordCloud

d = path.dirname('.')

text = open(path.join(d, 'constitution.txt')).read()
frequencies = [(u'知乎',5),(u'小段同学',4),(u'曲小花',3),(u'中文分词',2),(u'样例',1)]

#Generate a word cloud image 此处原为 text 方法，我们改用 frequencies
wordcloud = WordCloud().generate(text)
wordcloud = WordCloud().fit_words(frequencies)

# Display the generated image:
# the matplotlib way:
import matplotlib.pyplot as plt
plt.imshow(wordcloud)
plt.axis("off")

# take relative word frequencies into account, lower max_font_size
wordcloud = WordCloud(max_font_size=40, relative_scaling=.5).generate(text)
wordcloud = WordCloud(max_font_size=40, relative_scaling=.5).fit_words(frequencies)
plt.figure()
plt.imshow(wordcloud)
plt.axis("off")
plt.show()

# The pil way (if you don't have matplotlib)
image = wordcloud.to_image()
image.show()

# In[ ]:

model = RandomForestRegressor(n_estimator = 100, oob_score = TRUE, n_jobs = -1,random_state =50,max_features = "auto", min_samples_leaf = 50)
model.fit(X,y)
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import roc_auc_score
import pandas as pd
x = pd.read_csv("train.csv")
y = x.pop("Survived")
model =  RandomForestRegressor(n_estimator = 100 , oob_score = TRUE, random_state = 42)
model.fit(x(numeric_variable,y)

print "AUC - ROC : ", roc_auc_score(y,model.oob_prediction)

# In[23]:

from sklearn.ensemble import RandomForestClassifier #use RandomForestRegressor for regression problem
#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset
# Create Random Forest object
model= RandomForestClassifier(n_estimators=1000)
# Train the model using the training sets and check score
model.fit(X, y)
#Predict Output
predicted= model.predict(x_test)

# In[25]:

from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
import numpy as np

from sklearn.datasets import load_iris
iris=load_iris()
#print iris#iris的４个属性是：萼片宽度　萼片长度　花瓣宽度　花瓣长度　标签是花的种类：setosa versicolour virginica
print iris['target'].shape
rf=RandomForestRegressor()#这里使用了默认的参数设置
rf.fit(iris.data[:150],iris.target[:150])#进行模型的训练
#
#随机挑选两个预测不相同的样本
instance=iris.data[[100,109]]
print instance
print 'instance 0 prediction；',rf.predict(instance[0])
print 'instance 1 prediction；',rf.predict(instance[1])
print iris.target[100],iris.target[109]

# In[26]:

import numpy as np
import pylab as pl

x=np.random.uniform(1,100,1000)
y=np.log(x)+np.random.normal(0,.3,1000)

pl.scatter(x,y,s=1,label="log(x) with noise")

pl.plot(np.arange(1,100),np.log(np.arange(1,100)),c="b",label="log(x) true function")
pl.xlabel("x")
pl.ylabel("f(x)=log(x)")
pl.legend(loc="best")
pl.title('A Basic Log Function')
pl.show()

# In[29]:

from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import numpy as np

iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['is_train'] = np.random.uniform(0, 1, len(df)) <= .75
df['species'] = pd.Factor(iris.target, iris.target_names)
df.head()

train, test = df[df['is_train']==True], df[df['is_train']==False]

features = df.columns[:4]
clf = RandomForestClassifier(n_jobs=2)
y, _ = pd.factorize(train['species'])
clf.fit(train[features], y)

preds = iris.target_names[clf.predict(test[features])]
pd.crosstab(test['species'], preds, rownames=['actual'], colnames=['preds'])

# In[ ]:

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn.cross_validation import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_moons, make_circles, make_classification
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.lda import LDA
from sklearn.qda import QDA

h = .02  # step size in the mesh

names = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
"Random Forest", "AdaBoost", "Naive Bayes", "LDA", "QDA"]
classifiers = [
KNeighborsClassifier(3),
SVC(kernel="linear", C=0.025),
SVC(gamma=2, C=1),
DecisionTreeClassifier(max_depth=5),
RandomForestClassifier(max_depth=5, n_estimators=10, max_features=1),
AdaBoostClassifier(),
GaussianNB(),
LDA(),
QDA()]

X, y = make_classification(n_features=2, n_redundant=0, n_informative=2,
random_state=1, n_clusters_per_class=1)
rng = np.random.RandomState(2)
X += 2 * rng.uniform(size=X.shape)
linearly_separable = (X, y)

datasets = [make_moons(noise=0.3, random_state=0),
make_circles(noise=0.2, factor=0.5, random_state=1),
linearly_separable
]

figure = plt.figure(figsize=(27, 9))
i = 1
# iterate over datasets
for ds in datasets:
# preprocess dataset, split into training and test part
X, y = ds
X = StandardScaler().fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.4)

x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
np.arange(y_min, y_max, h))

# just plot the dataset first
cm = plt.cm.RdBu
cm_bright = ListedColormap(['#FF0000', '#0000FF'])
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
# Plot the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
# and testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright, alpha=0.6)
ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
i += 1

# iterate over classifiers
for name, clf in zip(names, classifiers):
ax = plt.subplot(len(datasets), len(classifiers) + 1, i)
clf.fit(X_train, y_train)
score = clf.score(X_test, y_test)

# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
if hasattr(clf, "decision_function"):
Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])
else:
Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]

# Put the result into a color plot
Z = Z.reshape(xx.shape)
ax.contourf(xx, yy, Z, cmap=cm, alpha=.8)

# Plot also the training points
ax.scatter(X_train[:, 0], X_train[:, 1], c=y_train, cmap=cm_bright)
# and testing points
ax.scatter(X_test[:, 0], X_test[:, 1], c=y_test, cmap=cm_bright,
alpha=0.6)

ax.set_xlim(xx.min(), xx.max())
ax.set_ylim(yy.min(), yy.max())
ax.set_xticks(())
ax.set_yticks(())
ax.set_title(name)
ax.text(xx.max() - .3, yy.min() + .3, ('%.2f' % score).lstrip('0'),
size=15, horizontalalignment='right')
i += 1

figure.subplots_adjust(left=.02, right=.98)
plt.show()

# In[1]:

background_image_filename='sushiplate.jpg'
mouse_image_filename='fugu.png'
#set the picture name
import pygame
#import pygame lib
from pygame.locals import *
from sys import exit
pygame.init()
screen=pygame.display.set_mode((640,480),0,32)
pygame.display.set_caption("hello world!")
background=pygame.image.load(background_image_filename).convert()
mouse_cursor=pygame.image.load(mouse_image_filename).convert_alpha()

while True:
for event in pygame.event.get():
if event.type==QUIT:
exit()

screen.blit(background, (0,0))
#
x,y=pygame.mouse.get_pos()
x-=mouse_cursor.get_width()/2
y-=mouse_cursor.get_height()/2
screen.blit(mouse_cursor,(x,y))
pygame.display.update()

# In[11]:

import matplotlib.pyplot as plt
import numpy as np

plt.xticks(np.arange(6))

plt.show()

# In[5]:

import numpy as np
import matplotlib.pyplot as plt
# Make some fake data.
a = b = np.arange(0,3, 0.02)
c = np.exp(a)
d = c[::-1]
#print c,d
# Create plots with pre-defined labels.
plt.plot(a, c, 'g--', label='Model length')
plt.plot(a, d, 'b:', label='Data length')
plt.plot(a, c+d, 'k', label='Total message length')
legend = plt.legend(loc='upper center',
shadow=True, fontsize='x-large')
# Put a nicer background color on the legend.
legend.get_frame().set_facecolor('#00FFCC')

plt.show()

# In[2]:

import numpy as np
import matplotlib.pyplot as plt
x1 = np.linspace(0.0, 5.0)

x2 = np.linspace(0.0, 2.0)
y1 = np.cos(2 * np.pi * x1) * np.exp(-x1)
y2 = np.cos(2 * np.pi * x2)
plt.subplot(2, 1, 1)
plt.plot(x1, y1, 'yo-')
plt.title('A tale of 2 subplots')
plt.ylabel('Damped oscillation')
plt.subplot(2, 1, 2)
plt.plot(x2, y2, 'r.-')
plt.xlabel('time (s)')
plt.ylabel('Undamped')

plt.show()

# In[1]:

import matplotlib.pyplot as plt
plt.subplot(211)
plt.plot([1,2,3], label="test1")
plt.plot([3,2,1], label="test2")
# Place a legend above this legend, expanding
#itself to fully use the given bounding box.
plt.legend(bbox_to_anchor=(0., 1.02, 1., .102),
loc=3, ncol=2, mode="expand",
borderaxespad=0.)
plt.subplot(223)
plt.plot([1,2,3], label="test1")
plt.plot([3,2,1], label="test2")
# Place a legend to the right of this smaller
# figure.
plt.legend(bbox_to_anchor=(1.05, 1), loc=2,
borderaxespad=0.)
plt.show()
# In[22]:

import matplotlib.pyplot as plt
line1, = plt.plot([1,2,3], label="Line 1",
linestyle='--')
line2, = plt.plot([3,2,1], label="Line 2",
linewidth=12)
# Create a legend for the first line.
first_legend = plt.legend(handles=[line1], loc=1)
# Add the legend manually to the current Axes.
ax = plt.gca().add_artist(first_legend)
# Create another legend for the second line.
plt.legend(handles=[line2], loc=4)
plt.show()

# In[35]:

import numpy as np
import matplotlib.pyplot as plt
mu, sigma = 100, 15
x = mu + sigma * np.random.randn(10000)
# the histogram of the data
n, bins, patches = plt.hist(x, 50, normed=1,
facecolor='b', alpha=0.75)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.title('Histogram of IQ')
#plt.text(60, .025, r'$\mu=100,\ \sigma=15$')
plt.text(60,0.020,r'$\mu=100,\ \sigma=15$')
plt.axis([40, 160, 0, 0.03])
plt.grid(False)
plt.show()

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