kastnerkyle

# Authors: Olivier Grisel <olivier.grisel@ensta.org>
#          James Bergstra <james.bergstra@umontreal.ca>
#          Vlad Niculae <vlad@vene.ro>
#
# License: BSD 3 Clause
# Updated to sklearn 0.14 by Kyle Kastner <kastnerkyle@gmail.com>

import numpy as np

from sklearn.decomposition import PCA

kastnerkyle

#!/usr/bin/env python
from pylearn2.models import mlp
from pylearn2.costs.mlp.dropout import Dropout
from pylearn2.training_algorithms import sgd, learning_rule
from pylearn2.termination_criteria import MonitorBased
from pylearn2.datasets import DenseDesignMatrix
from pylearn2.datasets import mnist
from pylearn2.train import Train
from pylearn2.train_extensions import best_params, window_flip
from pylearn2.space import VectorSpace

kastnerkyle

# Authors: Olivier Grisel <olivier.grisel@ensta.org>
#          James Bergstra <james.bergstra@umontreal.ca>
#          Vlad Niculae <vlad@vene.ro>
#          Kyle Kastner <kastnerkyle@gmail.com>
#          Samantha Massengill <sgmassengill@gmail.com>
# 
# License: BSD 3 clause

import numpy as np
from sklearn.decomposition import PCA

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import numpy as np
from scipy import sparse


def minibatch_indices(X, minibatch_size):
    minibatch_indices = np.arange(0, len(X), minibatch_size)
    minibatch_indices = np.asarray(list(minibatch_indices) + [len(X)])

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

#This code is for fun only! Use scipy.linalg.hadamard
import numpy as np
import matplotlib.pyplot as plt
import functools


def memoize(obj):

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.utils import gen_batches
from scipy.linalg import eigh
from scipy.linalg import svd
import numpy as np

# From sklearn master

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import numpy as np


class dhmm:
    def __init__(self, n_states, initial_prob=None,
                 n_iter=100, random_seed=1999):
        # Initial state probabilities p(s_0)=pi[s_0].

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import scipy.stats as st
import numpy as np

class gmmhmm:
    #This class converted with modifications from https://code.google.com/p/hmm-speech-recognition/source/browse/Word.m
    def __init__(self, n_states):
        self.n_states = n_states

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import numpy as np
from scipy import linalg

from sklearn.utils import array2d, as_float_array
from sklearn.utils.extmath import svd_flip
from sklearn.utils.testing import assert_array_almost_equal

kastnerkyle

# (C) Kyle Kastner, June 2014
# License: BSD 3 clause

import numpy as np

# Using data from http://www.mathsisfun.com/data/standard-deviation.html
X = np.array([600, 470, 170, 430, 300])

# Showing steps from basic to Welford's and batch
# See http://cpsc.yale.edu/sites/default/files/files/tr222.pdf