def init(X,y,r=0):
'''
'''
N, F = X.shape
C = len(np.unique(y))
w = np.random.rand(F, C)
b = np.ones(C)
para = {}
para['w'] = TeaSpoon.parameter(w, const=False)
para['b'] = TeaSpoon.parameter(b, const=False)
para['r'] = TeaSpoon.parameter(r, const=True)
return para
######################################################
# generate random dataset:
N = 600 # samples
F = 5 # features
C = 4 # labels
y = np.random.randint(0, C, N)
X = np.random.rand(N, F) * np.array([y+1]).T
######################################################
# initial model
w = np.random.uniform(-1,1,(F, C))
b = np.zeros(C)
p0 = {}
p0['w'] = TS.parameter(w, const=False)
p0['b'] = TS.parameter(b, const=False)
# define loss function
def loss(pi, X, y):
'''
compute loss (average negative conditional log likelihood)
'''
w = pi['w'].value
b = pi['b'].value
P = TT.nnet.softmax( T.dot(X, w) + b )
idx = TT.arange(y.shape[0]).astype('int64')
idy = y.astype('int64')
return -TT.mean(TT.log(P[idx, idy]))
