def get_reco(nfile, user, amount = 10) :
m = np.genfromtxt("ml-100k/u.item", dtype=[('myint','i8'),('mystring','S100')],
delimiter = "|")
#####
x,y = mmat.load_decomposition(nfile)
w = y.shape[1]
h = x.shape[0]
res = []
#Parcours des films
for i in xrange(w) :
res += [ (i+1, mmat.approx(x,y, user-1, i) ) ]
dtype = [('id', int),('rating', float) ]
res = np.array(res, dtype = dtype)
res = np.sort(res, order='rating')
##########
reco = []
for i in xrange(amount) :
reco += [ m[ (res[len(res) - i - 1])[0] +1][1] ]
return np.array(reco)
def mae(mat_test, X, Y) : H,L = mat_test.shape line = [ np.abs(mat_test[i,j] - mmat.approx(X,Y,i,j)) for i in xrange(H) for j in xrange(L) if mat_test[i,j] <> 0 ] return np.mean(line)
def f4(files): # Temps de prédiction
"""
Permet de calculer le tps de prédiction moyen
"""
tps = []
for f in files:
x, y = mmat.load_decomposition(f)
t1 = time.clock()
for i in xrange(100):
for j in xrange(100):
mmat.approx(x, y, i, j)
t2 = time.clock()
tps += [t2 - t1]
print tps
return tps
