mehdidc

from sklearn.svm import SVR

def smooth_image(x, y, z, w=100, h=100, model=SVR()):
    X = np.vstack((x, y)).T
    model.fit(X, z)
    x, y = np.meshgrid(
        np.linspace(x.min(), x.max(), w),
        np.linspace(y.min(), y.max(), h)
    )
    x = x.flatten()

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# Code adapted from https://github.com/kylemcdonald/Parametric-t-SNE
import numpy as np
import theano.tensor as T

def Hbeta(D, beta):
    P = np.exp(-D * beta)
    sumP = np.sum(P)
    H = np.log(sumP) + beta * np.sum(np.multiply(D, P)) / sumP
    P = P / sumP
    return H, P

mehdidc

"""
Implementation of pairwise ranking using scikit-learn LinearSVC

Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
    T. Graepel, K. Obermayer.

Authors: Fabian Pedregosa <fabian@fseoane.net>
         Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""

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from nolearn.lasagne import NeuralNet, BatchIterator
from lasagne import layers, nonlinearities, updates, init, objectives
import numpy as np

class EarlyStopping(object):

    def __init__(self, patience=100, criterion='valid_loss',
                 criterion_smaller_is_better=True):
        self.patience = patience
        if criterion_smaller_is_better is True:

mehdidc

from nolearn.lasagne import NeuralNet, BatchIterator
from lasagne import layers, nonlinearities, updates, init, objectives

from nolearn.lasagne.base import objective
from lasagne.objectives import aggregate
from lasagne.regularization import regularize_layer_params, l2, l1

import numpy as np

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from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
from keras.optimizers import Adadelta
from sklearn.datasets import make_blobs
from keras.utils.np_utils import to_categorical
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.regularizers import l2, l1

import matplotlib.pyplot as plt

mehdidc

from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
from keras.optimizers import Adadelta
from sklearn.datasets import make_blobs
from keras.utils.np_utils import to_categorical
from keras.callbacks import EarlyStopping, ModelCheckpoint
from keras.regularizers import l2, l1

import matplotlib.pyplot as plt

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from keras.layers import Dense, Input, Dropout
from keras.models import Sequential
from keras.optimizers import Adadelta
from sklearn.datasets import make_blobs
from keras.utils.np_utils import to_categorical
from keras.callbacks import EarlyStopping, ModelCheckpoint, LearningRateScheduler
from keras.regularizers import l2, l1

import matplotlib.pyplot as plt

mehdidc

from nolearn.lasagne import NeuralNet, BatchIterator
from lasagne import layers, nonlinearities, updates, init, objectives
import numpy as np
import theano

class EarlyStopping(object):

    def __init__(self, patience=100, criterion='valid_loss',
                 criterion_smaller_is_better=True):
        self.patience = patience

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from skopt import gp_minimize, forest_minimize, dummy_minimize, gbrt_minimize
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern

from skopt.benchmarks import branin

x0 = [[1, 2], [3, 4], [5, 6]]
y0 = map(branin, x0)

res = gp_minimize(branin,