def process_gd_algo(self, res_file):
start = time.clock()
train, train_index = self._get_train()
test, test_index = self._get_test()
gd = GDAlgorithm()
res = gd.process(train, test, test_index)
with open(res_file, "w") as f:
for r in res:
f.write('{} {} {}\n'.format(r[0], r[1], r[2]))
end = time.clock()
print "[log] process Matrix Decompose algorithm done, time spent %0.2f seconds." % (end - start)
def process_gd_algo(self, res_file):
start = time.clock()
train, train_index = self._get_train()
test, test_index = self._get_test()
gd = GDAlgorithm()
res = gd.process(train, test, test_index)
with open(res_file, "w") as f:
for r in res:
f.write('{} {} {}\n'.format(r[0], r[1], r[2]))
end = time.clock()
print "[log] process Matrix Decompose algorithm done, time spent %0.2f seconds." % (
end - start)
def compare_kr(self, res_file):
train, train_index = self._get_train()
test, test_index = self._get_test()
res_list = []
ks = [10, 20, 30, 40, 50, 60, 70]
rs = [0.001, 0.01, 0.025, 0.05, 0.075, 0.10]
for k in ks:
r = 0.01
gd = GDAlgorithm(k=k, lamb=r, compare=True)
res = gd.process(train, test, test_index)
res_list.append((k, res[len(res)-1][2]))
for r in rs:
k = 50
gd = GDAlgorithm(k=k, lamb=r, compare=True)
res = gd.process(train, test, test_index)
res_list.append((r, res[len(res)-1][2]))
with open(res_file, "w") as f:
for r in res_list:
f.write('{} {}\n'.format(r[0], r[1]))
def compare_kr(self, res_file):
train, train_index = self._get_train()
test, test_index = self._get_test()
res_list = []
ks = [10, 20, 30, 40, 50, 60, 70]
rs = [0.001, 0.01, 0.025, 0.05, 0.075, 0.10]
for k in ks:
r = 0.01
gd = GDAlgorithm(k=k, lamb=r, compare=True)
res = gd.process(train, test, test_index)
res_list.append((k, res[len(res) - 1][2]))
for r in rs:
k = 50
gd = GDAlgorithm(k=k, lamb=r, compare=True)
res = gd.process(train, test, test_index)
res_list.append((r, res[len(res) - 1][2]))
with open(res_file, "w") as f:
for r in res_list:
f.write('{} {}\n'.format(r[0], r[1]))
