def exponentielle():
debut = -1
fin = 4
pas = 0.1
x = np.arange(debut, fin, pas)
f = np.exp(x)
plt.plot(x, f)
lx = 'e = ' + str(np.exp(1))
plt.xlabel(lx)
plt.ylabel('f(x) = exp(x)')
plt.tile('Fct exponentielle')
plt.show()
def nxmx3(m):
sh = m.shape
m2 = m.copy() # working copy, might chance
assert (len(sh) == 2 or len(sh) == 3)
if len(sh) == 3:
if sh[2] == 3:
# matrix is already mxnx3
return m2
else:
# matrix is MxNxD with D!=3, best we can do:
m2 = pylab.mean(m2, 2)
# here, m2 is MxN...
return pylab.tile(m2.reshape(sh[0], sh[1], 1), (1, 1, 3))
def feature_scale(M, normalize=False, dbscale=False, norm=False, bels=False):
"""
::
Perform mutually-orthogonal scaling operations, otherwise return identity:
normalize [False]
dbscale [False]
norm [False]
"""
if not (normalize or dbscale or norm or bels):
return M
else:
X = M.copy() # don't alter the original
if norm:
X = X / P.tile(P.sqrt((X * X).sum(0)), (X.shape[0], 1))
if normalize:
X = _normalize(X)
if dbscale or bels:
X = P.log10(P.clip(X, 0.0001, X.max()))
if dbscale:
X = 20 * X
return X
plt.ylabel("Runs Scored")
plt.show(),
plt.scatter(W, DOUB)
plt.xlabel("Wins")
plt.ylabel("Doubles hit")
plt.show()
obs = range(30)
plt.scatter(Ypred, Wt)
plt.xlabel("Predicted Wins")
plt.ylabel("Observed Wins")
plt.show()
plt.hist(Residuals)
plt.tile("Residuals Distribution", 16)
plt.show()
def average(my_list):
sumall = sum(my_list)
average = sumall / len(my_list)
return average
average = average(Residuals)
print "Residuals mean = ",average
variance = np.var(Residuals)
print "Residuals Variance =",variance
plt.ylabel("Runs Scored")
plt.show(),
plt.scatter(W, DOUB)
plt.xlabel("Wins")
plt.ylabel("Doubles hit")
plt.show()
obs = range(30)
plt.scatter(Ypred, Wt)
plt.xlabel("Predicted Wins")
plt.ylabel("Observed Wins")
plt.show()
plt.hist(Residuals)
plt.tile("Residuals Distribution", 16)
plt.show()
def average(my_list):
sumall = sum(my_list)
average = sumall / len(my_list)
return average
average = average(Residuals)
print "Residuals mean = ", average
variance = np.var(Residuals)
print "Residuals Variance =", variance
#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date : 2017-10-16 17:04:57 # @Author : Bob Liao # @Email : [email protected] # @Link : https://github.com/coderchaser # @Path : E:\Code\Python\sublimeText\hipython_cousera\matplotlib_.py import numpy as np from matplotlib import pyplot as plt from matplotlib import pylab as pb x=np.linspace(0,1) pb.tile('ss') pb.plot(np.sin(4*np.pi*x)*np.exp(-5*x),'bo') pb.show()