giuseppebonaccorso

#!/usr/bin/env python
# encoding: utf-8

import tweepy #https://github.com/tweepy/tweepy
import csv

#Twitter API credentials
consumer_key = ""
consumer_secret = ""
access_key = ""

giuseppebonaccorso

'''
Neural artistic styler
Based on: Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, "A Neural Algorithm of Artistic Style", arXiv:1508.06576
Examples: https://www.bonaccorso.eu
See also: https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py
Giuseppe Bonaccorso (https://www.bonaccorso.eu)
'''

from __future__ import print_function

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

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 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

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

giuseppebonaccorso

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)

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

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