Python BernoulliRBM.score_samples方法代码示例

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def testRBM():
  X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]])
  print X
  model = BernoulliRBM(n_components=2)
  model.fit(X)
  print dir(model)
  print model.transform(X)
  print model.score_samples(X)
  print model.gibbs

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def test_small_sparse_partial_fit():
    for sparse in [csc_matrix, csr_matrix]:
        X_sparse = sparse(Xdigits[:100])
        X = Xdigits[:100].copy()

        rbm1 = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, random_state=9)
        rbm2 = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, random_state=9)

        rbm1.partial_fit(X_sparse)
        rbm2.partial_fit(X)

        assert_almost_equal(rbm1.score_samples(X).mean(), rbm2.score_samples(X).mean(), decimal=0)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def run_test(params, model):
    
    if model == "rf":
        n_tree, mtry = params
        print "# Trees: ", n_tree
        print "mtry: ", mtry
        rf = RandomForestClassifier(n_estimators= int(n_tree), verbose = True, 
                                n_jobs = -1, max_features= int(mtry))
        rf.fit(X, y)
        modelPred = rf.predict(X)
    elif model == "svm":
        C, kernel = params
        print "# Cost: ", C
        print "kernel: ", kernel
        svmod = SVC(int(C), kernel)
        svmod.fit(X, y)
        modelPred = svmod.predict(X)
    elif model == "knn":
        k = params
        print "# k: ", k
        knnmod = KNeighborsClassifier(int(k))
        knnmod.fit(X, y)
        modelPred =knnmod.predict(X)
    elif model == "NeuralNetwork":
        n_components, learning_rate, batch_size, n_iter = params
        print "# n_components: ", n_components
        print "# learning_rate: ", learning_rate
        print "# batch_size: ", batch_size
        print "# n_iter: ", n_iter 
        nnmod = BernoulliRBM(int(n_components), learning_rate, int(batch_size), int(n_iter))
        nnmod.fit(X, y)
        modelPred =nnmod.score_samples(X)
    
    accuError = AccuracyErrorCalc(y, modelPred)
    return accuError

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def test_score_samples():
    """Test score_samples (pseudo-likelihood) method."""
    # Assert that pseudo-likelihood is computed without clipping.
    # http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression
    rng = np.random.RandomState(42)
    X = np.vstack([np.zeros(1000), np.ones(1000)])
    rbm1 = BernoulliRBM(n_components=10, batch_size=2,
                        n_iter=10, random_state=rng)
    rbm1.fit(X)
    assert_true((rbm1.score_samples(X) < -300).all())

    # Sparse vs. dense should not affect the output. Also test sparse input
    # validation.
    rbm1.random_state = 42
    d_score = rbm1.score_samples(X)
    rbm1.random_state = 42
    s_score = rbm1.score_samples(lil_matrix(X))
    assert_almost_equal(d_score, s_score)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def test_fit():
    X = Xdigits.copy()

    rbm = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=10, n_iter=7, random_state=9)
    rbm.fit(X)

    assert_almost_equal(rbm.score_samples(X).mean(), -21.0, decimal=0)

    # in-place tricks shouldn't have modified X
    assert_array_equal(X, Xdigits)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def test_score_samples():
    """Check that the pseudo likelihood is computed without clipping.

    http://fa.bianp.net/blog/2013/numerical-optimizers-for-logistic-regression/
    """
    rng = np.random.RandomState(42)
    X = np.vstack([np.zeros(1000), np.ones(1000)])
    rbm1 = BernoulliRBM(n_components=10, batch_size=2,
                        n_iter=10, random_state=rng)
    rbm1.fit(X)
    assert((rbm1.score_samples(X) < -300).all())

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def test_partial_fit():
    X = Xdigits.copy()
    rbm = BernoulliRBM(n_components=64, learning_rate=0.1, batch_size=20, random_state=9)
    n_samples = X.shape[0]
    n_batches = int(np.ceil(float(n_samples) / rbm.batch_size))
    batch_slices = np.array_split(X, n_batches)

    for i in range(7):
        for batch in batch_slices:
            rbm.partial_fit(batch)

    assert_almost_equal(rbm.score_samples(X).mean(), -21.0, decimal=0)
    assert_array_equal(X, Xdigits)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def test_score_samples():
    """Test score_samples (pseudo-likelihood) method."""
    # Assert that pseudo-likelihood is computed without clipping.
    # See Fabian's blog, http://bit.ly/1iYefRk
    rng = np.random.RandomState(42)
    X = np.vstack([np.zeros(1000), np.ones(1000)])
    rbm1 = BernoulliRBM(n_components=10, batch_size=2, n_iter=10, random_state=rng)
    rbm1.fit(X)
    assert_true((rbm1.score_samples(X) < -300).all())

    # Sparse vs. dense should not affect the output. Also test sparse input
    # validation.
    rbm1.random_state = 42
    d_score = rbm1.score_samples(X)
    rbm1.random_state = 42
    s_score = rbm1.score_samples(lil_matrix(X))
    assert_almost_equal(d_score, s_score)

    # Test numerical stability (#2785): would previously generate infinities
    # and crash with an exception.
    with np.errstate(under="ignore"):
        rbm1.score_samples(np.arange(1000) * 100)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
def estimate_n_components():
    X = load_data('gender/male')
    X = X.astype(np.float32) / 256
    n_comp_list = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200]
    scores = []
    for n_comps in n_comp_list:
        rbm = BernoulliRBM(random_state=0, verbose=True)
        rbm.learning_rate = 0.06
        rbm.n_iter = 50
        rbm.n_components = 100
        rbm.fit(X)
        score = rbm.score_samples(X).mean()
        scores.append(score)
    plt.figure()
    plt.plot(n_comp_list, scores)
    plt.show()
    return n_comp_list, scores

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import score_samples [as 别名]
import numpy as np
from sklearn.neural_network import BernoulliRBM
from sklearn import cross_validation

#Read from data file
with open("dataset") as textFile:
	lines = [line.split() for line in textFile]
a = np.array(lines, dtype=float)

dataPoints = np.array(a[:, [1, 2, 3]])
target = np.array(a[:, 0])

model = BernoulliRBM()
last_score = 0
last_partition = 0
for i in range(2, 10):
	x_train, x_test, y_train, y_test = cross_validation.train_test_split(dataPoints, target, test_size = float(i)/10.0, random_state = 0)
	model.fit(x_train, y_train)
	if (model.score_samples(x_test, y_test)) > last_score:
		last_score = model.score_samples(x_test, y_test)
		last_partition = (i+1)/10
x_train, x_test, y_train, y_test = cross_validation.train_test_split(dataPoints, target, test_size = last_partition, random_state = 0)
model.fit(x_train, y_train)
print model.score_samples(x_test, y_test)
print last_score