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@takatakamanbou takatakamanbou/00_Vision.md Secret
Last active May 7, 2019

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0 0.7550326945263522
0.05 1.007767522485821
0.1 0.6361953553367321
0.15 0.6959238424062661
0.2 0.595485181368133
0.25 0.4831892240046355
0.3 0.159575705011379
0.35 0.186501916036992
0.4 0.1567768077057974
0.45 0.04182438372911931
0.5 -0.06982713339289053
0.55 0.08544102980639576
0.6 -0.1007904952533244
0.65 -0.1277469644723259
0.7 -0.02542025832150743
0.75 -0.411322875976739
0.8 0.01309389848625916
0.85 -0.1196592829342933
0.9 0.07144698650328071
0.95 -0.1983213666516035
import numpy as np
import leastsquares as ls
dat = np.loadtxt('dat20170410.txt')
N = dat.shape[0]
print('# N =', N)
x = dat[:, 0]
D = 1 # D is the degree of the polynomial to be fitted
X = np.zeros((N, D+1))
X[:, 0] = 1
for i in range(1, D+1):
X[:, i] = x**i
print(X)
print('# X.shape =', X.shape)
y = dat[:, 1]
print(y)
print('# y.shape =', y.shape)
w = ls.solve(X, y)
print(w)
import numpy as np
import leastsquares as ls
import gendat190428 as gendat
# if you have matplotlib, you can set the following variable to True.
haveMatplotlib = False
def makeDataMatrix(x, D):
N = x.shape[0]
X = np.zeros((N, D+1))
X[:, 0] = 1
for i in range(1, D+1):
X[:, i] = x**i
return X
ndata = 30
sigma = 0.5
# learning (training) data
datL = gendat.gendat(ndata, seed=0, sigma=sigma)
# test data
datT = gendat.gendat(ndata, seed=1, sigma=sigma)
# fitting a D-th degree polynomial by least squares method
D = 10
X = makeDataMatrix(datL[:, 0], D)
y = datL[:, 1]
print('# X.shape =', X.shape)
print('# y.shape =', y.shape)
w = ls.solve(X, y)
print('# estimated coefficients = ', w)
# mean squared error for the learning data
y_est = np.dot(X, w)
msqe = np.mean((y - y_est)**2)
print('# mean squared error (L) = ', msqe)
# mean squared error for the test data
X = makeDataMatrix(datT[:, 0], D)
y = datT[:, 1]
y_est = np.dot(X, w)
msqe = np.mean((y - y_est)**2)
print('# mean squared error (T) = ', msqe)
# true (noiseless) data & more...
datTrue = gendat.gendat(1000)
X = makeDataMatrix(datTrue[:, 0], D)
y = datTrue[:, 1]
y_est = np.dot(X, w)
if haveMatplotlib:
import matplotlib.pyplot as plt
plt.xlim(-2, 3)
plt.ylim(-5, 10)
plt.plot(datTrue[:, 0], datTrue[:, 1], color="blue") # true data
plt.scatter(datL[:, 0], datL[:, 1], color="red") # noisy data
plt.plot(datTrue[:, 0], y_est, color="green") # estimated curve
plt.show()
else:
# The graph can be plotted by using gnuplot as follows
# gnuplot> plot [-2:3][-5:10] "true.txt" w l, "data.txt" w p, "estimated.txt" w l
np.savetxt('true.txt', datTrue)
A = np.hstack((datL, datT[:, 1, np.newaxis]))
np.savetxt('data.txt', A)
B = np.vstack((datTrue[:, 0], y_est)).T
np.savetxt('estimated.txt', B)
import numpy as np
import image
N = 231 # 総データ数
D = 64*64 # データの次元数
dat = np.empty((N, D)) # データ行列
for i in range(N):
# img.shape は (64, 64)
img = image.imread('face231/img%03d.png' % i)
# それを 4096 次元ベクトルに reshape して dat[i, :] に代入
dat[i, :] = img.reshape(-1)
print(dat.shape)
# ラベル. 0 が人間女性,1 が人間男性,2 が猫
lab = np.loadtxt('face231/label.txt', dtype = int)
print(lab)
# 最初の131枚を学習用,残り100枚をテスト用とする
datL = dat[:131, :]
labL = lab[:131]
datT = dat[131:, :]
labT = lab[131:]
print('# 学習データの数は', datL.shape[0])
print('# うち人間女性は', datL[labL == 0].shape[0])
print('# うち人間男性は', datL[labL == 1].shape[0])
print('# うち猫は', datL[labL == 2].shape[0])
img = np.mean(datL[labL == 2, :], axis = 0).reshape((64, 64))
image.imsave('hoge.png', img)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def gendat(n, seed = 0, nsig = 0.0):
x = np.linspace(-1.5, 2.5, num = n)
y = x*x*x - x*x - x + 1.0
np.random.seed(seed)
y += nsig * np.random.randn(n)
X = np.vstack((x, y)).T
return X
if __name__ == '__main__':
n = 20
seed = 0
X = gendat(n, seed = seed, nsig = 0.5)
np.savetxt('hoge.txt', X)
import numpy as np
def gendat(n, seed = 0, sigma = 0.0):
x = np.linspace(-1.5, 2.5, num = n)
y = x*x*x - x*x - x + 1.0
np.random.seed(seed)
y += sigma * np.random.randn(n)
X = np.vstack((x, y)).T
return X
if __name__ == '__main__':
# if you have matplotlib, you can set the following variable to True.
haveMatplotlib = False
seed = 0
data_noisy = gendat(30, seed=seed, sigma=0.5)
data_true = gendat(1000, seed=seed, sigma=0)
if haveMatplotlib:
import matplotlib.pyplot as plt
plt.xlim(-2, 3)
plt.ylim(-5, 10)
plt.plot(data_true[:, 0], data_true[:, 1], color="blue")
plt.scatter(data_noisy[:, 0], data_noisy[:, 1], color="red")
plt.show()
else:
# The graph can be plotted by using gnuplot as follows
# gnuplot> plot [-2:3][-5:10] "true.txt" w l, "data.txt" w p
np.savetxt('true.txt', data_true)
np.savetxt('data.txt', data_noisy)
import scipy.misc as spmisc
import numpy as np
def imread(fn):
img = spmisc.imread(fn)
# spmisc.imread returns a 3-D or 2-D array with dtype = uint8
# so its type should be converted
# converting to int (pixel vaules are in [0, 255])
#img2 = img.astype(np.int)
# converting to float (pixel vaules are in [0, 1])
img2 = img.astype(np.float) / 255
return img2
def imsave(fn, img):
# spmisc.imsave can handle all of the above dtypes (uint8, int, float)
spmisc.imsave(fn, img)
if __name__ == '__main__':
img = imread('blackuni3.png')
print(img.shape, img.dtype)
hoge = 1.0 - img
imsave('hoge.png', hoge)
hoge2 = img[:, ::-1, :]
imsave('hoge2.png', hoge2)
hoge3 = (hoge + hoge2)/2
imsave('hoge3.png', hoge3)
img = imread('blackuni3-gray.png')
print(img.shape, img.dtype)
hogeG = 1.0 - img
imsave('hogeG.png', hogeG)
import numpy as np
### solving the linear least squares problem (1)
#
# X.shape is assumed to be (N, D+1)
# y.shape is assumed to be (N,)
#
def solve(X, y):
A = np.dot(X.T, X)
b = np.dot(X.T, y)
# x is the solution of the equation Ax = b
x = np.linalg.solve(A, b)
return x
### solving the linear least squares problem (2)
#
def solve2(X, y):
A = np.dot(X.T, X)
b = np.dot(X.T, y)
# rv[0] is the solution minimizing ||Ax - b||^2
rv = np.linalg.lstsq(A, b)
return rv[0]
if __name__ == '__main__':
x = np.array([0, 4, 8, 20])
y = np.array([30, 31, 29, 28])
y -= y[0]
N = x.shape[0]
print('# N =', N)
X = np.vstack((np.ones(N), x)).T
print(X)
print('# X.shape =', X.shape)
print(y)
print('# y.shape =', y.shape)
w = solve(X, y)
#w = solve2(X, y)
print('# estimated parameters =', w)
b, a = w # w[0] is b and w[1] is a
print('# fitted equation is y =', a, '*x +', b)
print(a * 36 + b + 30)
# -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
def getData(nclass, seed = None):
assert nclass == 2 or nclass == 3
if seed != None:
np.random.seed(seed)
# 2次元の spherical な正規分布3つからデータを生成
X0 = 0.10 * np.random.randn(200, 2) + [ 0.3, 0.3 ]
X1 = 0.10 * np.random.randn(200, 2) + [ 0.7, 0.6 ]
X2 = 0.05 * np.random.randn(200, 2) + [ 0.3, 0.7 ]
# それらのラベル用のarray
lab0 = np.zeros(X0.shape[0], dtype = int)
lab1 = np.zeros(X1.shape[0], dtype = int) + 1
lab2 = np.zeros(X2.shape[0], dtype = int) + 2
# X (入力データ), label (クラスラベル), t(教師信号) をつくる
if nclass == 2:
X = np.vstack((X0, X1))
label = np.hstack((lab0, lab1))
t = np.zeros(X.shape[0])
t[label == 1] = 1.0
else:
X = np.vstack((X0, X1, X2))
label = np.hstack((lab0, lab1, lab2))
t = np.zeros((X.shape[0], nclass))
for ik in range(nclass):
t[label == ik, ik] = 1.0
return X, label, t
if __name__ == '__main__':
import matplotlib
import matplotlib.pyplot as plt
K = 3
X, lab, t = getData(K)
fig = plt.figure()
plt.xlim(-0.2, 1.2)
plt.ylim(-0.2, 1.2)
ax = fig.add_subplot(1, 1, 1)
ax.set_aspect(1)
ax.scatter(X[lab == 0, 0], X[lab == 0, 1], color = 'red')
ax.scatter(X[lab == 1, 0], X[lab == 1, 1], color = 'green')
if K == 3:
ax.scatter(X[lab == 2, 0], X[lab == 2, 1], color = 'blue')
plt.show()
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