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 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

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
import tensorflow as tf

from sklearn.datasets import make_blobs

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

# Create dataset
nb_samples=2000

giuseppebonaccorso

from scipy.sparse import dok_matrix
from sklearn.metrics.pairwise import pairwise_distances

import numpy as np

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

# Create a dummy user-item dataset
nb_users = 1000

giuseppebonaccorso

from sklearn.datasets import make_classification

import numpy as np

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

def sigmoid(arg):
    return 1.0 / (1.0 + np.exp(-arg))

giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

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

nb_patterns = 4
pattern_width = 4
pattern_height = 4
max_iterations = 10

giuseppebonaccorso

import matplotlib.pyplot as plt
import numpy as np

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

nb_patterns = 4
pattern_width = 4
pattern_height = 4
max_iterations = 100

giuseppebonaccorso

from sklearn.datasets import load_digits

import numpy as np

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

# Load MNIST dataset
X, Y = load_digits(return_X_y=True)
X /= 255.0

giuseppebonaccorso

import numpy as np

from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

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

nb_samples = 5000