def main():
global data
# baseline using equilibrium equations
data = pd.read_csv('serbian.csv')
W0 = ndl.ndl(data)
diff = np.zeros_like(W0)
W = np.zeros_like(W0)
# simulate learning for R individuals
R = 1000
now = time()
P = Pool(6)
for i, W1 in P.imap_unordered(simulate, xrange(R)):
diff += abs(W1 - W0)
W += W1
print >> sys.stderr, i, time() - now
diff = diff / R
W = W / R
# get cue-outcome co-occurrence frequencies
cues = DictVectorizer(dtype=int, sparse=False)
D = cues.fit_transform([ndl.explode(c) for c in data.Cues])
out = DictVectorizer(dtype=int, sparse=False)
X = out.fit_transform([ndl.explode(c) for c in data.Outcomes
]) * data.Frequency[:, np.newaxis]
O = np.zeros_like(W0)
for i in xrange(len(X)):
for nz in np.nonzero(D[i]):
O[nz] += X[i]
# save results
np.savez('serbian-rw', diff=diff, W0=W0.as_matrix(), O=O, W=W)
def main():
global data
# baseline using equilibrium equations
data = pd.read_csv('serbian.csv')
W0 = ndl.ndl(data)
diff = np.zeros_like(W0)
W = np.zeros_like(W0)
# simulate learning for R individuals
R = 1000
now = time()
P = Pool(6)
for i,W1 in P.imap_unordered(simulate,xrange(R)):
diff += abs(W1 - W0)
W += W1
print >>sys.stderr,i,time()-now
diff = diff / R
W = W / R
# get cue-outcome co-occurrence frequencies
cues = DictVectorizer(dtype=int,sparse=False)
D = cues.fit_transform([ndl.explode(c) for c in data.Cues])
out = DictVectorizer(dtype=int,sparse=False)
X = out.fit_transform([ndl.explode(c) for c in data.Outcomes]) * data.Frequency[:,np.newaxis]
O = np.zeros_like(W0)
for i in xrange(len(X)):
for nz in np.nonzero(D[i]):
O[nz] += X[i]
# save results
np.savez('serbian-rw',diff=diff,W0=W0.as_matrix(),O=O,W=W)
def main():
data = pd.read_csv('lexample.csv')
W0 = ndl.ndl(data)
diff = np.zeros_like(W0)
for i in xrange(10):
W = ndl.rw(data, M=10000)
diff += abs(W - W0)
diff = diff / 10.
print diff.max(), diff.min(), np.mean(diff)
