def persistence():
print "Persistence"
numpy.random.seed(42)
n_dims = 2
n_class = 12
size = 100
labels = numpy.random.random_integers(0, 256, size)
samples = numpy.zeros((size, n_dims), dtype=type)
print "Generating data"
for i in range(0, size):
t = 6.28 * numpy.random.random_sample()
samples[i][0] = 2 * labels[i] + 1.5 * numpy.cos(t)
samples[i][1] = 2 * labels[i] + 1.5 * numpy.sin(t)
print "Creating dense classifier"
classifier = algo.svm_dense(0, n_dims=n_dims, seed=42)
print "Adding sample vectors to dense classifier"
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier.add_sample(float(y), x)
print "Pickling dense classifier"
cPickle.dump(classifier, open('test', 'wb'))
classifier = cPickle.load(open('test', 'rb'))
print "Training dense classifier"
classifier.train(gamma=1, C=10, eps=1e-1)
print "Predicting with dense classifier"
print classifier.predict(samples[0])
print "Creating 0/1 classifier"
classifier01 = algo.svm_01(n_dims=n_dims, seed=42)
print "Adding sample vectors to 0/1 classifier"
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier01.add_sample(float(y), x)
print "Training 0/1 classifier"
classifier01.train(gamma=1. / 3., C=100, eps=1e-1)
print "Pickling 0/1 classifier"
cPickle.dump(classifier01, open('test', 'wb'))
classifier01 = cPickle.load(open('test', 'rb'))
print "Predicting with 0/1 classifier"
print classifier01.predict(numpy.array(samples[0], dtype=type))
def persistence():
print "Persistence"
numpy.random.seed(42)
n_dims = 2
n_class = 12
size = 100
labels = numpy.random.random_integers(0, 256, size)
samples = numpy.zeros((size, n_dims), dtype=type)
print "Generating data"
for i in range(0, size):
t = 6.28 * numpy.random.random_sample()
samples[i][0] = 2 * labels[i] + 1.5 * numpy.cos(t)
samples[i][1] = 2 * labels[i] + 1.5 * numpy.sin(t)
print "Creating dense classifier"
classifier = algo.svm_dense(0, n_dims = n_dims, seed=42)
print "Adding sample vectors to dense classifier"
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier.add_sample(float(y), x)
print "Pickling dense classifier"
cPickle.dump(classifier, open('test', 'wb'))
classifier = cPickle.load(open('test', 'rb'))
print "Training dense classifier"
classifier.train(gamma = 1, C = 10, eps=1e-1)
print "Predicting with dense classifier"
print classifier.predict(samples[0])
print "Creating 0/1 classifier"
classifier01 = algo.svm_01(n_dims = n_dims, seed=42)
print "Adding sample vectors to 0/1 classifier"
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier01.add_sample(float(y), x)
print "Training 0/1 classifier"
classifier01.train(gamma = 1./3., C = 100, eps=1e-1)
print "Pickling 0/1 classifier"
cPickle.dump(classifier01, open('test', 'wb'))
classifier01 = cPickle.load(open('test', 'rb'))
print "Predicting with 0/1 classifier"
print classifier01.predict(numpy.array(samples[0], dtype=type))
def testPersistentSize(self):
for _ in range(2):
# Multiple of 8 because of sse.
nDims = 32
nClass = _RGEN.randint(4, 8)
size = _RGEN.randint(20, 50)
labels = _RGEN.random_integers(0, nClass - 1, size)
samples = _RGEN.normal(size=(size, nDims)).astype(_DTYPE)
classifier = svm_dense(0, nDims, seed=_SEED, probability=True)
for y, xList in zip(labels, samples):
x = np.array(xList, dtype=_DTYPE)
classifier.add_sample(float(y), x)
classifier.train(gamma=1.0 / 3.0, C=100, eps=1e-1)
classifier.cross_validate(2, gamma=0.5, C=10, eps=1e-3)
s = classifier.__getstate__()
print("nDims=",
nDims,
"nClass=",
nClass,
"n_vectors=",
size,
end=' ')
print("dense:", len(s), classifier.persistent_size(), end=' ')
self.assertEqual(len(s), classifier.persistent_size())
classifier01 = svm_01(0, nDims, seed=_SEED, probability=True)
for y, xList in zip(labels, samples):
x = np.array(xList, dtype=_DTYPE)
classifier01.add_sample(float(y), x)
classifier01.train(gamma=1.0 / 3.0, C=100, eps=1e-1)
classifier01.cross_validate(2, gamma=0.5, C=10, eps=1e-3)
s = classifier01.__getstate__()
print("0/1", len(s), classifier01.persistent_size())
self.assertEqual(len(s), classifier01.persistent_size())
def testPersistentSize(self):
for _ in range(2):
# Multiple of 8 because of sse.
nDims = 32
nClass = _RGEN.randint(4, 8)
size = _RGEN.randint(20, 50)
labels = _RGEN.random_integers(0, nClass - 1, size)
samples = _RGEN.normal(size=(size, nDims)).astype(_DTYPE)
classifier = svm_dense(0, nDims, seed=_SEED, probability=True)
for y, xList in zip(labels, samples):
x = np.array(xList, dtype=_DTYPE)
classifier.add_sample(float(y), x)
classifier.train(gamma=1.0/3.0, C=100, eps=1e-1)
classifier.cross_validate(2, gamma=0.5, C=10, eps=1e-3)
s = classifier.__getstate__()
print "nDims=", nDims, "nClass=", nClass, "n_vectors=", size,
print "dense:", len(s), classifier.persistent_size(),
self.assertEqual(len(s), classifier.persistent_size())
classifier01 = svm_01(0, nDims, seed=_SEED, probability=True)
for y, xList in zip(labels, samples):
x = np.array(xList, dtype=_DTYPE)
classifier01.add_sample(float(y), x)
classifier01.train(gamma=1.0/3.0, C=100, eps=1e-1)
classifier01.cross_validate(2, gamma=0.5, C=10, eps=1e-3)
s = classifier01.__getstate__()
print "0/1", len(s), classifier01.persistent_size()
self.assertEqual(len(s), classifier01.persistent_size())
def persistence():
print("Persistence")
numpy.random.seed(42)
n_dims = 2
n_class = 12
size = 100
labels = numpy.random.random_integers(0, 256, size)
samples = numpy.zeros((size, n_dims), dtype=type)
print("Generating data")
for i in range(0, size):
t = 6.28 * numpy.random.random_sample()
samples[i][0] = 2 * labels[i] + 1.5 * numpy.cos(t)
samples[i][1] = 2 * labels[i] + 1.5 * numpy.sin(t)
print("Creating dense classifier")
classifier = algo.svm_dense(0, n_dims=n_dims, seed=42)
print("Adding sample vectors to dense classifier")
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier.add_sample(float(y), x)
print("Serializing dense classifier")
# CHH proto is not supported
# schema = classifier.getSchema()
# with open("test", "w+b") as f:
# # Save
# proto = schema.new_message()
# classifier.write(proto)
# proto.write(f)
#
# # Load
# f.seek(0)
# proto2 = schema.read(f)
# classifier = algo.svm_dense.read(proto2)
print("Training dense classifier")
classifier.train(gamma=1, C=10, eps=1e-1)
print("Predicting with dense classifier")
print(classifier.predict(samples[0]))
print("Creating 0/1 classifier")
classifier01 = algo.svm_01(n_dims=n_dims, seed=42)
print("Adding sample vectors to 0/1 classifier")
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier01.add_sample(float(y), x)
print("Training 0/1 classifier")
classifier01.train(gamma=1. / 3., C=100, eps=1e-1)
print("Serializing 0/1 classifier")
# CHH proto is not supported
# schema = classifier01.getSchema()
# with open("test", "w+b") as f:
# # Save
# proto = schema.new_message()
# classifier01.write(proto)
# proto.write(f)
#
# # Load
# f.seek(0)
# proto2 = schema.read(f)
# classifier01 = algo.svm_01.read(proto2)
print("Predicting with 0/1 classifier")
print(classifier01.predict(numpy.array(samples[0], dtype=type)))
def persistence():
print "Persistence"
numpy.random.seed(42)
n_dims = 2
n_class = 12
size = 100
labels = numpy.random.random_integers(0, 256, size)
samples = numpy.zeros((size, n_dims), dtype=type)
print "Generating data"
for i in range(0, size):
t = 6.28 * numpy.random.random_sample()
samples[i][0] = 2 * labels[i] + 1.5 * numpy.cos(t)
samples[i][1] = 2 * labels[i] + 1.5 * numpy.sin(t)
print "Creating dense classifier"
classifier = algo.svm_dense(0, n_dims = n_dims, seed=42)
print "Adding sample vectors to dense classifier"
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier.add_sample(float(y), x)
print "Serializing dense classifier"
schema = classifier.getSchema()
with open("test", "w+b") as f:
# Save
proto = schema.new_message()
classifier.write(proto)
proto.write(f)
# Load
f.seek(0)
proto2 = schema.read(f)
classifier = algo.svm_dense.read(proto2)
print "Training dense classifier"
classifier.train(gamma = 1, C = 10, eps=1e-1)
print "Predicting with dense classifier"
print classifier.predict(samples[0])
print "Creating 0/1 classifier"
classifier01 = algo.svm_01(n_dims = n_dims, seed=42)
print "Adding sample vectors to 0/1 classifier"
for y, x_list in zip(labels, samples):
x = numpy.array(x_list, dtype=type)
classifier01.add_sample(float(y), x)
print "Training 0/1 classifier"
classifier01.train(gamma = 1./3., C = 100, eps=1e-1)
print "Serializing 0/1 classifier"
schema = classifier01.getSchema()
with open("test", "w+b") as f:
# Save
proto = schema.new_message()
classifier01.write(proto)
proto.write(f)
# Load
f.seek(0)
proto2 = schema.read(f)
classifier01 = algo.svm_01.read(proto2)
print "Predicting with 0/1 classifier"
print classifier01.predict(numpy.array(samples[0], dtype=type))
