Giuseppe Bonaccorso giuseppebonaccorso

## hetero-encoder.py
import matplotlib.pyplot as plt
import multiprocessing
import numpy as np
import tensorflow as tf

from keras.datasets import cifar10

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

## som-olivetti-cupy.py
import cupy as cp
import matplotlib.pyplot as plt
import numpy as np

from sklearn.datasets import fetch_olivetti_faces

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

## passive_aggressive_regression.py
import matplotlib.pyplot as plt
import numpy as np

from sklearn.datasets import make_regression

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

nb_samples = 500
nb_features = 4

## passive_aggressive_classification.py
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

## rubner-tavan-pca-network.py
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

## brain-state-in-a-box.py
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

## hopfield.py
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

## quickprop.py
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))

## model_free_collaborative_filtering.py
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

## fim.py
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
	import matplotlib.pyplot as plt
	import multiprocessing
	import numpy as np
	import tensorflow as tf

	from keras.datasets import cifar10

	# Set random seed (for reproducibility)
	np.random.seed(1000)
	tf.set_random_seed(1000)
	import cupy as cp
	import matplotlib.pyplot as plt
	import numpy as np

	from sklearn.datasets import fetch_olivetti_faces

	# Set random seed for reproducibility
	np.random.seed(1000)
	cp.random.seed(1000)
	import matplotlib.pyplot as plt
	import numpy as np

	from sklearn.datasets import make_regression

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

	nb_samples = 500
	nb_features = 4
	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
	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
	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
	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