x = np.zeros((n_data, 64 * 64), dtype=float)
y = np.zeros((n_data), dtype=int)
for i, add in enumerate(adds):
xp = imread(add)
xp = xp.reshape(-1)
xp = np.array(xp, dtype=int)
x[i] = xp
if 'B' in add:
y[i] = 0
else:
y[i] = 1
return x, y
X_test, y_test = batch()
add = './res/'
mce.ch_mkdir(add)
if j < 6:
res = full_test(X_test, X_test, y_test, j, levs, o_list)
with open(add + dr_name[j] + '_' + str(i) + '.pkl', 'wb') as f:
pickle.dump(res, f)
else:
res = sk_check(X_test, X_test, y_test, o_list)
with open(add + 'sk_' + str(i) + '.pkl', 'wb') as f:
pickle.dump(res, f)
import sys
import numpy as np
import drama as drm
#import matplotlib.pylab as plt
#from matplotlib import gridspec
n_ftrs = 100
noise = 0.8
scl = 0.00
sft = 0.00
i_sig = int(sys.argv[1])
n_train = int(sys.argv[2])
nn = int(sys.argv[3])
dir_add = './' + sys.argv[0][:-3] + '_res/'
drm.ch_mkdir(dir_add)
if os.path.exists(dir_add + str(i_sig) + '_' + str(n_train) + '_' + str(nn) +
'.pickle'):
exit()
x = np.linspace(0, 1, n_ftrs)
X, y = drm.synt_mix(i_sig,
n_ftrs,
x=x,
n_inlier=1000,
n_outlier=5,
sigma=noise,
n1=scl,
n2=sft,
n3=scl,