Python BernoulliRBM.fit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 fit [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 fit [as 别名]
def rbm_001():
    s = 15
    crop = 150
    n_patches = 400000
    rf_size = 5

    train_x_crop_scale = CropScaleImageTransformer(training=True,
                                                   result_path='data/data_train_crop_{}_scale_{}.npy'.format(crop, s),
                                                   crop_size=crop,
                                                   scaled_size=s,
                                                   n_jobs=-1,
                                                   memmap=True)

    patch_extractor = models.KMeansFeatures.PatchSampler(n_patches=n_patches,
                                                         patch_size=rf_size,
                                                         n_jobs=-1)
    images = train_x_crop_scale.transform()
    images = images.reshape((images.shape[0], 15 * 15 * 3))

    # rbm needs inputs to be between 0 and 1
    scaler = MinMaxScaler()
    images = scaler.fit_transform(images)

    # Training takes a long time, says 80 seconds per iteration, but seems like longer
    # And this is only with 256 components
    rbm = BernoulliRBM(verbose=1)
    rbm.fit(images)

    train_x = rbm.transform(images)
    train_y = classes.train_solutions.data

    # 0.138 CV on 50% of the dataset
    wrapper = ModelWrapper(models.Ridge.RidgeRFEstimator, {'alpha': 500, 'n_estimators': 500}, n_jobs=-1)
    wrapper.cross_validation(train_x, train_y, sample=0.5, parallel_estimator=True)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def neural_network_classify(train_data,train_label,test_data):
    # nnc=MLPClassifier(algorithm='l-bfgs', alpha=1e-5, hidden_layer_sizes=(5, 2), random_state=1)
    nnc=BernoulliRBM(random_state=0, verbose=True)
    nnc.fit(train_data, ravel(train_label))
    test_label=ncc.predict(test_data)

    save_result(test_label,'sklearn_neural_network_classify_Result.csv')  
    return test_label 

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def Bernoulli(X_train, X_test, y_train, y_test):
    mod = BernoulliRBM(random_state=0, verbose=True)
    mod.fit(X_train, y_train)
    print "Done training"
    bernoulli_labels = mod.predict(X_test)
    print "Done testing"
    bernoulli_score = mod.score(X_test, y_test)
    return bernoulli_score, bernoulli_labels

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def test_rbm_verbose():
    rbm = BernoulliRBM(n_iter=2, verbose=10)
    old_stdout = sys.stdout
    sys.stdout = StringIO()
    try:
        rbm.fit(Xdigits)
    finally:
        sys.stdout = old_stdout

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def test_transform():
    X = Xdigits[:100]
    rbm1 = BernoulliRBM(n_components=16, batch_size=5, n_iter=5, random_state=42)
    rbm1.fit(X)

    Xt1 = rbm1.transform(X)
    Xt2 = rbm1._mean_hiddens(X)

    assert_array_equal(Xt1, Xt2)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def test_gibbs_smoke():
    """ just seek if we don't get NaNs sampling the full digits dataset """
    rng = np.random.RandomState(42)
    X = Xdigits
    rbm1 = BernoulliRBM(n_components=42, batch_size=10,
                        n_iter=20, random_state=rng)
    rbm1.fit(X)
    X_sampled = rbm1.gibbs(X)
    assert_all_finite(X_sampled)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [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 fit [as 别名]
def train_rbm(X, n_components=100, n_iter=10):
    X = X.astype(np.float64)
    X = (X - np.min(X, 0)) / (np.max(X, 0) + 0.0001)  # scale to [0..1]
    rbm = BernoulliRBM(random_state=0, verbose=True)
    rbm.learning_rate = 0.06
    rbm.n_iter = n_iter
    rbm.n_components = n_components
    rbm.fit(X)
    return rbm

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def test_gibbs_smoke():
    """Check if we don't get NaNs sampling the full digits dataset.
    Also check that sampling again will yield different results."""
    X = Xdigits
    rbm1 = BernoulliRBM(n_components=42, batch_size=40, n_iter=20, random_state=42)
    rbm1.fit(X)
    X_sampled = rbm1.gibbs(X)
    assert_all_finite(X_sampled)
    X_sampled2 = rbm1.gibbs(X)
    assert_true(np.all((X_sampled != X_sampled2).max(axis=1)))

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def test_sample_hiddens():
    rng = np.random.RandomState(0)
    X = Xdigits[:100]
    rbm1 = BernoulliRBM(n_components=2, batch_size=5, n_iter=5, random_state=42)
    rbm1.fit(X)

    h = rbm1._mean_hiddens(X[0])
    hs = np.mean([rbm1._sample_hiddens(X[0], rng) for i in range(100)], 0)

    assert_almost_equal(h, hs, decimal=1)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [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 fit [as 别名]
class DeepRbmMnistClassifier:

    def __init__(self):
        self.n_components_first = 500
        self.n_components_second = 500
        self.n_components_third = 2000
        self.n_iter_first = 20
        self.n_iter_second = 20
        self.n_iter_third = 20
        self.learning_rate_first = 0.06
        self.learning_rate_second = 0.06
        self.learning_rate_third = 0.06
        self.verbose = True

    def label_to_feature(self,y):
        feature = [0]*10
        feature[y] = 1
        return feature

    def fit(self,X,y):
        self.rbm_1 = BernoulliRBM(verbose=self.verbose,
                            n_components=self.n_components_first,
                            n_iter=self.n_iter_first,
                            learning_rate=self.learning_rate_first)
        self.rbm_2 = BernoulliRBM(verbose=self.verbose,
                            n_components=self.n_components_second,
                            n_iter=self.n_iter_second,
                            learning_rate=self.learning_rate_second)
        self.first_pipeline = Pipeline(steps=[('rbm_1',self.rbm_1), ('rbm_2',self.rbm_2)])
        self.first_pipeline.fit(X,y)

        # TODO improve. Look at how it is done in classify
        new_features = []
        for example,label in zip(X,y):
            transformed = self.first_pipeline.transform(example)[0]
            new_features.append(np.concatenate((transformed,self.label_to_feature(label))))

        self.rbm_3 = BernoulliRBM(verbose=self.verbose,
                            n_components=self.n_components_third,
                            n_iter=self.n_iter_third,
                            learning_rate=self.learning_rate_third)
        self.rbm_3.fit(new_features,y)

    def classify(self,X):
        transformed = self.first_pipeline.transform(X)
        transformed = np.concatenate((transformed,[[0]*10]*len(transformed)),axis=1)

        # The inverse of rbm_3 to go from hidden layer to visible layer
        rbm_aux = BernoulliRBM()
        rbm_aux.intercept_hidden_ = self.rbm_3.intercept_visible_
        rbm_aux.intercept_visible_ = self.rbm_3.intercept_hidden_
        rbm_aux.components_ = np.transpose(self.rbm_3.components_)
        results = rbm_aux.transform(self.rbm_3.transform(transformed))
        results = results[:,-10:]
        return np.argmax(results,axis=1)

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [as 别名]
def run_auto():
    X = load_data('gender/male')
    X = X.astype(np.float32) / 256
    rbm = BernoulliRBM(random_state=0, verbose=True)
    rbm.learning_rate = 0.06
    rbm.n_iter = 20
    rbm.n_components = 2000
    rbm.fit(X)
    cimgs = [comp.reshape(100, 100) for comp in rbm.components_]
    smartshow(cimgs[:12])
    return rbm

# 需要导入模块: from sklearn.neural_network import BernoulliRBM [as 别名]
# 或者: from sklearn.neural_network.BernoulliRBM import fit [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())