In [1]:
import numpy as np
import matplotlib.pyplot as plt
In [2]:
data = np.load('serbian-rw.npz')
diff = data['diff']
W0 = data['W0']
O = data['O']
W = data['W']
In [3]:
n = diff.shape[0]
m = diff.shape[1]
d, w, o, w0 = [], [], [], []
for i in xrange(n):
for j in xrange(m):
if O[i,j]:
d.append(diff[i,j])
w0.append(W0[i,j])
o.append(O[i,j])
w.append(W[i,j])
diff = np.array(d)
W = np.array(w)
O = np.array(o)
W0 = np.array(w0)
In [4]:
(diff/W0).mean()
Out[4]:
0.27038610307469491
In [5]:
plt.axis([0,1,0,1])
plt.scatter(W0,W)
Out[5]:
<matplotlib.collections.PathCollection at 0x29bc190>
In [20]:
plt.axis([0,2000,0,1])
plt.scatter(O,diff/W0)
Out[20]:
<matplotlib.collections.PathCollection at 0x42ab1d0>
In [6]:
plt.axis([0,1,0,1])
plt.scatter(W0,diff)
Out[6]:
<matplotlib.collections.PathCollection at 0x2b45690>
In [7]:
plt.axis([0,2000,0,1])
plt.scatter(O,diff)
Out[7]:
<matplotlib.collections.PathCollection at 0x2d7f190>
In [8]:
plt.axis([0,2000,0,1])
plt.scatter(O,W0)
Out[8]:
<matplotlib.collections.PathCollection at 0x2d95ad0>
In [9]:
plt.axis([0,2000,0,1])
plt.scatter(O,W)
Out[9]:
<matplotlib.collections.PathCollection at 0x2f87350>
In [14]:
diff.flat[:1000]
Out[14]:
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In [ ]:
Content source: rmalouf/learning
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