def der_error(w,l,sample,sampleSize):
d = std.sparse_mult(l, w)
sum = {}
for i in range(sampleSize):
label = sample[i].get(-1,0)
example = std.take_out_label(sample[i])
if (label*(std.sparse_dot(w,example)) < 1):
sum = std.sparse_vsum(sum,std.sparse_mult(-label,example))
#print("der_err summ part = " + str(sum))
dcost = std.sparse_vsum(d,sum)
return dcost
def guide_get_feature(stub):
# A variable to count the number of iteration of the client, which must coincide with the epoch in the server.
it = 1
# We make a first call to the server to get the data : after that call, vect is the data set. Then we store it.
vect = stub.GetFeature(route_guide_pb2.Vector(poids="pret"))
# We convert the set of data in the good format.
data = vect.poids.split("<samples>")
dataSampleSet = std.str2datadict(data[0])
data2 = data[1].split("<#compo>")
numSamples = int(data2[0])
nbCompo = int(data2[1])
# This second call serves to get the departure vector.
vect = stub.GetFeature(route_guide_pb2.Vector(poids="getw0"))
info = vect.poids.split("<<||>>")
vect.poids = info[0]
step = float(info[1])
# Vector of the rests
m = {}
while (vect.poids != 'stop'):
print("iteration : " + str(it))
# Gradient descent on the sample.
nw = sgd.descent(dataSampleSet, std.str2dict(vect.poids), numSamples, step)
# Updating of m
stepedGradient = std.sparse_mult(step,nw)
m = std.sparse_vsum(m,stepedGradient)
# Get the nbCompo biggest values in m, put it in g and remove them of m
g = std.infiniteNormInd(m,nbCompo)
# The result is sent to the server.
strVect = std.dict2str(g)
vect.poids = strVect + "<bytes>" + str(sys.getsizeof(strVect))
vect = stub.GetFeature(route_guide_pb2.Vector(poids=vect.poids))
if (vect.poids != 'stop'):
info = vect.poids.split("<<||>>")
vect.poids = info[0]
step = float(info[1])
it += 1
print(vect)
data[0][-1] = 1
data[1][-1] = -1
data[2][-1] = 1
print('')
print("############## Test of the sparse scalar product. ##############")
print('')
spdsp = std.sparse_dot(spV1, spV2)
print("spdsp = " + str(spdsp))
print('')
print("################ Test of the sparse sum. #################")
print('')
spdsu = std.sparse_vsum(spV1, spV2)
print("spdsum = " + str(spdsu))
empty = std.sparse_vsum({}, spV1)
print("empty = " + str(empty))
print('')
print("############### Test of the sparse map. ###############")
print('')
def opp(x):
return (-x)
spdmap = std.sparse_map(opp, spV1)