def test_normal_mixture_hard(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel(
np.arange(-3, 4),
np.random.uniform(1, 2, 7).round(2)),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel(np.arange(-3, 4) * 0.1,
np.ones(7),
updates=["mu", "sigma"]),
gradient_descent=GradientDescent(np.array([0.3, 0.1, 0.3]).reshape(
(-1, 1)),
inertia=0.9,
annealing=2000,
last_learning_rate=0.001),
)
for i in range(5000):
competition.iteration()
params = competition.generatives[-1]._params
print params.shape
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.2)
def test_normal_1000(self):
np.random.seed(0)
size_batch = 1000
adversarials = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(1, 2),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(0, 1, updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.03, 0.9),
)
for i in range(200):
adversarials.iteration()
params = adversarials.generatives[-1]._params
true_params = adversarials.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.02)
def test_normal_1000(self):
np.random.seed(0)
size_batch = 1000
adversarials = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(1, 2),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(0, 1, updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.03, 0.9),
)
for i in range(200):
adversarials.iteration()
params = adversarials.generatives[-1]._params
true_params = adversarials.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.02)
def test_normal_mixture(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel([-3, 3], [1, 1]),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel(
[-1, 1], [1, 1], updates=["mu", "sigma"]),
gradient_descent=GradientDescent(
0.1, inertia=0.9, annealing=1000, last_learning_rate=0.01),
)
for i in range(2000):
competition.iteration()
params = competition.generatives[-1]._params
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.1)
def test_normal_mixture(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel([-3, 3], [1, 1]),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel([-1, 1], [1, 1],
updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.1,
inertia=0.9,
annealing=1000,
last_learning_rate=0.01),
)
for i in range(2000):
competition.iteration()
params = competition.generatives[-1]._params
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.1)
def test_normal_mixture_hard(self):
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel(
np.arange(-3, 4), np.random.uniform(1, 2, 7).round(2)),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel(
np.arange(-3, 4) * 0.1, np.ones(7), updates=["mu", "sigma"]),
gradient_descent=GradientDescent(
np.array([0.3, 0.1, 0.3]).reshape((-1, 1)), inertia=0.9,
annealing=2000, last_learning_rate=0.001),
)
for i in range(5000):
competition.iteration()
params = competition.generatives[-1]._params
print params.shape
true_params = competition.true_model._params
np.testing.assert_allclose(params, true_params, 0, 0.2)
from competition import AdversarialCompetition
from models import GenerativeNormalMixtureModel
from gradient_descent import GradientDescent
logging.basicConfig(
format="[%(filename)s:%(lineno)s - %(funcName)20s() ] %(message)s",
level="INFO")
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalMixtureModel([-3, 3], [1, 1]),
discriminative=pipeline.make_pipeline(preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalMixtureModel([-1, 1], [1, 1],
updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.01, 0.5),
)
print(competition)
for i in range(1000):
if i % 200 == 0:
competition.plot()
plt.show()
pass
competition.iteration()
print("final model %s" % competition.generatives[-1])
from gradient_descent import GradientDescent
pyplot.ioff()
logging.basicConfig(
format=
"[%(filename)s:%(lineno)s - %(funcName)15s() %(asctime)-15s ] %(message)s",
level=logging.DEBUG)
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(1, 2),
discriminative=pipeline.make_pipeline(preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(0, 1, updates=["mu", "sigma"]),
gradient_descent=GradientDescent(0.03, inertia=0.0, annealing=100),
)
print(competition)
for i in range(500):
if i % 50 == 0:
competition.plot()
pyplot.savefig('file.png')
pyplot.close()
pass
competition.iteration()
print("final model %s" % competition.generatives[-1])
from competition import AdversarialCompetition
from models import GenerativeNormalModel
from gradient_descent import GradientDescent
logging.basicConfig(format="[%(filename)s:%(lineno)s - %(funcName)15s() %(asctime)-15s ] %(message)s", level=logging.DEBUG)
np.random.seed(0)
size_batch = 1000
competition = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(1, 2),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(
0, 1, updates=["mu", "sigma"]),
gradient_descent=GradientDescent(
0.03, inertia=0.0, annealing=100),
)
print(competition)
for i in range(200):
if i % 50 == 0:
competition.plot()
plt.show()
pass
competition.iteration()
np.random.seed(0)
size_batch = 100
mu_param = 3
sigma_param = 1.0
sample_size = 1000
true_data1 = GenerativeNormalModel(mu_param,sigma_param).random(sample_size).reshape(-1)
true_data2 = GenerativeNormalModel(-mu_param,sigma_param).random(sample_size).reshape(-1)
true_data = np.concatenate((true_data1,true_data2))
competition1 = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(mu_param, sigma_param),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(
2.5,2.1,updates=["mu", "sigma"]),
gradient_descent=GradientDescent(
0.03, inertia=0.0, annealing=100),
x_dataset = true_data1
)
competition2 = AdversarialCompetition(
size_batch=size_batch,
true_model=GenerativeNormalModel(-mu_param, sigma_param),
discriminative=pipeline.make_pipeline(
preprocessing.PolynomialFeatures(4),
linear_model.LogisticRegression()),
generative=GenerativeNormalModel(
-2.5,2.1,updates=["mu", "sigma"]),
gradient_descent=GradientDescent(