t1 = timeit.default_timer()
r = func(*args, **keyArgs)
t2 = timeit.default_timer()
times.append(t2-t1)
print (min(times))
return r
return st_func
p = args.size[0]
n = args.size[1]
X = rand(p,n)
Xp = rand(p,n)
y = rand(p,n)
regr = linear_model.Ridge()
@st_time
def test_fit(X,y):
regr.fit(X,y)
@st_time
def test_predict(X):
regr.predict(X)
print (','.join([args.batchID, args.arch, args.prefix, "Ridge.fit", coreString(args.num_threads), "Double", "%sx%s" % (p,n)]), end=',')
test_fit(X, y)
print (','.join([args.batchID, args.arch, args.prefix, "Ridge.prediction", coreString(args.num_threads), "Double", "%sx%s" % (p,n)]), end=',')
test_predict(Xp)
@st_time
def test_transform(Xp, pca_result, eigenvalues, eigenvectors):
return pca_transform_daal(pca_result,
Xp,
n_components,
X.shape[0],
eigenvalues,
eigenvectors,
whiten=args.whiten)
print(','.join([
args.batchID, args.arch, args.prefix, "PCA.fit",
coreString(args.num_threads), "Double",
"%sx%s" % (p, n)
]),
end=',')
res = test_fit(X)
print(','.join([
args.batchID, args.arch, args.prefix, "PCA.transform",
coreString(args.num_threads), "Double",
"%sx%s" % (p, n)
]),
end=',')
tr = test_transform(Xp, res[0], res[1], res[2])
if args.write_results:
np.save('pca_daal4py_X.npy', X)
np.save('pca_daal4py_Xp.npy', Xp)