def __init__(self):
self.dict = {3: [1, 2, 4, 5, 8]}
self.d = os.getcwd() + '\\Datas'
self.fn = FN.FN(d=self.d + '\\RawXlsx')
self.fn.analyzeExtensions()
self.filesName = []
for i in self.fn.filesName:
self.filesName.append(self.fn.analyzeFN(i))
pass
def __getFilesName(self, dir, typeList):
filesName = []
fn = FN.FN(dir)
fn.analyzeExtensions()
for i in fn.filesName:
temp = fn.analyzeFN(i)
if temp[2] in typeList:
filesName.append(temp)
return filesName
def __init__(self):
self.dict = {3: [1, 2, 4, 5, 8]}
self.wordsType = [
'vi.', 'vt.', 'a.', 'n.', 'ad.', 'v.', 'prep.', 'suffix', 'infml.'
]
self.wordsType2 = []
self.d = os.getcwd() + '\\Datas'
self.fn = FN.FN(d=self.d + '\\RawXlsx')
self.fn.analyzeExtensions()
self.filesName = []
for i in self.fn.filesName:
self.filesName.append(self.fn.analyzeFN(i))
pass
def __init__(self):
self.d = r'D:\0COCO\本科\英语四六级\六级词汇词根+联想记忆法(MP3+文本)'
self.fn = FN.FN(d=self.d)
self.fn.analyzeExtensions()
self.filesName = []
self.wordsType = [
'vt.', 'adj.', 'n.', 'v.', 'vi.', 'adv.', 'prep.', 'conj.'
]
self.wordsType2 = []
for i in self.fn.filesName:
temp = self.fn.analyzeFN(i)
if temp[2] == '.lrc':
self.filesName.append(temp)
pass
def __init__(self, recreate=False):
self.d = os.getcwd() + '\\Datas'
self.fn = FN.FN(d=self.d + '\\ModifyXlsx')
self.fn.analyzeExtensions()
self.font = Font(name='Arial',
size=11,
bold=False,
italic=False,
vertAlign=None,
underline='none',
strike=False,
color='FF000000')
self.space = [' ', ' ']
if (recreate):
md = ModifyData.MD()
md.test()
self.spy = Spyder.Spy()
if (not os.path.exists(self.d + '\\Mp3')):
os.mkdir(self.d + '\\Mp3')
pass
import PDP
data=sio.loadmat('ex7data1')
X=data['X']
#Part One: Load Example Dataset
print 'One: ======== Load Example Dataset1 ... '
plt.plot(X[:,0],X[:,1],'bo')
plt.axis(xmin=0.5,xmax=6.5,ymin=2,ymax=8)
plt.title('Example Dataset1')
#Part Two: Principal Component Analysis
print 'Two: ================ Running PCA on example dataset...'
result=FN.featureNormalize(X)
X_norm=result[0]
mu=result[1]
res=PCA.pca(X_norm)
U=res[0]
S=res[1]
S=np.eye(S.shape[0])*S
print 'Top eigenvector: '
print 'U[:,0] = %f %f ' % (U[0,0],U[1,0])
print '(You should expect to see -0.707107, -0.707107)'
tmp1=mu+1.5*np.dot(S[0,0],U[:,0].transpose())
tmp2=mu+1.5*np.dot(S[1,1],U[:,1].transpose())
import psyneulink as pnl
import FN
FN = pnl.Composition(name='FN')
FNpop_0 = pnl.IntegratorMechanism(name='FNpop_0',
function=pnl.FitzHughNagumoIntegrator(
name='Function_FitzHughNagumoIntegrator',
d_v=1,
initial_v=-1))
FN.add_node(FNpop_0)
import FN
import CF
import os
if __name__ == '__main__':
wordDir = r'D:\temp\project\01Tests\3-fightPlane\Classes'
scrEnc = 'utf-8'
desEnc = 'gbk'
Type = ['.cpp', '.h', '.lrc']
fn1 = FN.FN(wordDir)
fn1.analyzeExtensions()
c = 1
error = []
oldFiles = []
for i in fn1.filesName:
t = fn1.analyzeFN(i)
if t[2] in Type:
try:
with open(t[0] + t[1] + t[2], 'r', encoding=scrEnc) as f:
text = f.read()
fileDir = t[0] + t[1] + t[2]
if (fileDir.find('_coco56_GBK_To_UTF-8') == -1):
print('c=', c, sep='')
print(t)
c += 1
oldFiles.append(fileDir)
newFileDir = t[0] + t[1] + '_coco56_GBK_To_UTF-8' + t[2]
if (not os.path.exists(newFileDir)):
with open(newFileDir, 'w', encoding=desEnc) as f2:
f2.write(text)
except UnicodeDecodeError:
import FN
import CF
import os
if __name__ == '__main__':
fn1 = FN.FN(r'D:\0COCO\System\桌面\SpeedPan\C++ Primer视频教程（初级中级高级）')
fn1.analyzeExtensions()
d = fn1.filesNum
d = sorted(d.items(), key=lambda item:item[1], reverse=True)
'''
这里的d.items()实际上是将d转换为可迭代对象，
迭代对象的元素为 （‘lilee’,25）、（‘wangyan’,21）、（‘liqun’,32）、（‘lidaming’,19），
items()方法将字典的元素 转化为了元组，
而这里key参数对应的lambda表达式的意思则是选取元组中的第二个元素作为比较参数
（如果写作key=lambda item:item[0]的话则是选取第一个元素作为比较对象，也就是key值作为比较对象。
lambda x:y中x表示输出参数，y表示lambda 函数的返回值），
所以采用这种方法可以对字典的value进行排序。
注意排序后的返回值是一个list，而原字典中的名值对被转换为了list中的元组。
'''
print(d)
import FN
import CF
import os
if __name__ == '__main__':
fn1 = FN.FN(
r'H:\OneDrive - revolutionize B2C bandwidth\视频教程\架构H:\OneDrive - revolutionize B2C bandwidth\视频教程\架构'
)
fn1.analyzeExtensions()
d = fn1.filesNum
d = sorted(d.items(), key=lambda item: item[1], reverse=True)
'''
这里的d.items()实际上是将d转换为可迭代对象，
迭代对象的元素为 （‘lilee’,25）、（‘wangyan’,21）、（‘liqun’,32）、（‘lidaming’,19），
items()方法将字典的元素 转化为了元组，
而这里key参数对应的lambda表达式的意思则是选取元组中的第二个元素作为比较参数
（如果写作key=lambda item:item[0]的话则是选取第一个元素作为比较对象，也就是key值作为比较对象。
lambda x:y中x表示输出参数，y表示lambda 函数的返回值），
所以采用这种方法可以对字典的value进行排序。
注意排序后的返回值是一个list，而原字典中的名值对被转换为了list中的元组。
'''
print(d)
result=LC.learningCurve(tmp1,y,tmp2,yval,Lambda)
l1=plt.plot(np.array(range(m))+1,result[0],'r',np.array(range(m))+1,result[1],'b')
plt.title('Learning curve for linear regression')
plt.legend(l1,('Train','Cross Validataion'),loc=1)
plt.xlabel('Number of training examples')
plt.ylabel('Error')
plt.show()
#Part Five: Feature Mapping for Polynomial Regression
print 'Five: ==========================Feature Mapping for Polynomial Regression...'
p=8
X_poly=PF.polyFeatures(X,p)
result=FN.featureNormalize(X_poly)
X_poly=result[0]
mu=result[1]
sigma=result[2]
X_poly=np.hstack((np.ones((m,1)),X_poly))
Xtest=data['Xtest']
X_poly_test=PF.polyFeatures(Xtest,p)
X_poly_test=X_poly_test-mu
X_poly_test=X_poly_test/sigma
X_poly_text=np.hstack((np.ones((X_poly_test.shape[0],1)),X_poly_test))
X_poly_val=PF.polyFeatures(Xval,p)
@author: aa
"""
import numpy as np
import FN
import GDM
import NE
print 'Loading data ...'
data=np.loadtxt('ex1data2.txt',delimiter=',')
X=data[:,0:2]
y=data[:,2]
m=len(y)
print 'Normalizing Features ...'
tmp=FN.featureNormalize(X)
X=tmp[0]
mu=tmp[1]
sigma=tmp[2]
X=np.hstack((np.ones((m,1)), X))
print 'Running gradient descent ...'
alpha = 0.1
num_iters=1000
theta=np.zeros((3,1))
tmp=GDM.gradientDescentMulti(X,y,theta,alpha,num_iters)
theta=tmp[0]