giuseppebonaccorso

from sklearn.datasets import make_blobs
from sklearn.neighbors import NearestNeighbors

import matplotlib.pyplot as plt
import multiprocessing
import numpy as np
import time

# Set random seed (for reproducibility)
np.random.seed(1000)

giuseppebonaccorso

import numpy as np

from Queue import Queue


class HMM:
    def __init__(self, transition_matrix, observation_matrix, delay, initial_state):
        self.TM = transition_matrix
        self.OM = observation_matrix
        self.delay = delay

giuseppebonaccorso

import numpy as np

from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

# Set random seed for reproducibility
np.random.seed(1000)

# Create and scale dataset
X, _ = make_blobs(n_samples=500, centers=2, cluster_std=5.0, random_state=1000)

giuseppebonaccorso

import numpy as np

from sklearn.datasets import make_blobs
from sklearn.preprocessing import StandardScaler

# Set random seed for reproducibility
np.random.seed(1000)

# Create and scale dataset
X, _ = make_blobs(n_samples=500, centers=2, cluster_std=5.0, random_state=1000)

giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

from scipy.integrate import odeint

# Set random seed (for reproducibility)
np.random.seed(1000)

# Start and end time (in milliseconds)
tmin = 0.0

giuseppebonaccorso

import keras.backend as K
import multiprocessing
import tensorflow as tf

from gensim.models.word2vec import Word2Vec

from keras.callbacks import EarlyStopping
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Flatten
from keras.layers.convolutional import Conv1D

giuseppebonaccorso

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
from sklearn.metrics.pairwise import pairwise_distances

import multiprocessing

# Set random seed for reproducibility
np.random.seed(1000)

# Generate a dummy dataset

giuseppebonaccorso

import numpy as np
import tensorflow as tf

# Set random seed for reproducibility
np.random.seed(1000)

nb_users = 5000
nb_products = 2000
nb_factors = 500
max_rating = 5

giuseppebonaccorso

'''
Mathematical expression learning experiment

Giuseppe Bonaccorso (https://www.bonaccorso.eu)
Based on: http://machinelearningmastery.com/learn-add-numbers-seq2seq-recurrent-neural-networks/
'''

from __future__ import print_function

from keras.models import Sequential

giuseppebonaccorso

'''
Cifar-10 classification
Original dataset and info: https://www.cs.toronto.edu/~kriz/cifar.html for more information
See: https://www.bonaccorso.eu/2016/08/06/cifar-10-image-classification-with-keras-convnet/ for further information
'''
 
from __future__ import print_function

import numpy as np