ror, scala, jetty, erlang, thrift, mongrel, comet server, my-sql, memchached, varnish, kestrel(mq), starling, gizzard, cassandra, hadoop, vertica, munin, nagios, awstats
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# read shorten (shn) files into numpy arrays | |
from __future__ import division | |
import os.path as path | |
import os | |
from subprocess import call | |
import struct | |
import tempfile | |
def shnread(shnfile): |
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import time | |
import gc | |
import numpy as np | |
def theano_memory_hack(func_exp, local_vars, | |
input_exps=('input',), | |
msize_best=None, | |
msize_start=1024, msize_factor=2, | |
verbose=False): |
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import numpy as np | |
import numpy | |
import theano | |
import theano.tensor as T | |
from theano import function, config, shared, sandbox | |
from theano import ProfileMode | |
from sklearn import cluster, datasets | |
import matplotlib.pyplot as plt | |
def rsom(data, cluster_num, alpha, epochs = -1, batch = 1, verbose = False): |
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# (C) Mathieu Blondel, November 2013 | |
# License: BSD 3 clause | |
import numpy as np | |
from scipy.linalg import svd | |
def frequent_directions(A, ell, verbose=False): | |
""" | |
Return the sketch of matrix A. |
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""" | |
bitmap utils and much of the ctc code modified | |
From Shawn Tan, Rakesh and Mohammad Pezeshki | |
""" | |
# Author: Kyle Kastner | |
# License: BSD 3-clause | |
from theano import tensor | |
from scipy import linalg | |
import theano | |
import numpy as np |
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# Alec Radford, Indico, Kyle Kastner | |
# License: MIT | |
""" | |
Convolutional VAE in a single file. | |
Bringing in code from IndicoDataSolutions and Alec Radford (NewMu) | |
Additionally converted to use default conv2d interface instead of explicit cuDNN | |
""" | |
import theano | |
import theano.tensor as T | |
from theano.compat.python2x import OrderedDict |
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"""A simple implementation of a greedy transition-based parser. Released under BSD license.""" | |
from os import path | |
import os | |
import sys | |
from collections import defaultdict | |
import random | |
import time | |
import pickle | |
SHIFT = 0; RIGHT = 1; LEFT = 2; |
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""" | |
This is a batched LSTM forward and backward pass | |
""" | |
import numpy as np | |
import code | |
class LSTM: | |
@staticmethod | |
def init(input_size, hidden_size, fancy_forget_bias_init = 3): |
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# Mathieu Blondel, May 2012 | |
# License: BSD 3 clause | |
import numpy as np | |
def euclidean_distances(X, Y=None, Y_norm_squared=None, squared=False): | |
XX = np.sum(X * X, axis=1)[:, np.newaxis] | |
YY = np.sum(Y ** 2, axis=1)[np.newaxis, :] | |
distances = np.dot(X, Y.T) | |
distances *= -2 |
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