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from multiprocessing import Pool | |
from functools import partial | |
def _pickle_method(method): | |
func_name = method.im_func.__name__ | |
obj = method.im_self | |
cls = method.im_class | |
if func_name.startswith('__') and not func_name.endswith('__'): #deal with mangled names | |
cls_name = cls.__name__.lstrip('_') | |
func_name = '_' + cls_name + func_name |
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#!/usr/bin/env python | |
""" | |
Example of using Keras to implement a 1D convolutional neural network (CNN) for timeseries prediction. | |
""" | |
from __future__ import print_function, division | |
import numpy as np | |
from keras.layers import Convolution1D, Dense, MaxPooling1D, Flatten | |
from keras.models import Sequential |
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# (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 |
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- word2vec https://arxiv.org/abs/1310.4546 | |
- sentence2vec, paragraph2vec, doc2vec http://arxiv.org/abs/1405.4053 | |
- tweet2vec http://arxiv.org/abs/1605.03481 | |
- tweet2vec https://arxiv.org/abs/1607.07514 | |
- author2vec http://dl.acm.org/citation.cfm?id=2889382 | |
- item2vec http://arxiv.org/abs/1603.04259 | |
- lda2vec https://arxiv.org/abs/1605.02019 | |
- illustration2vec http://dl.acm.org/citation.cfm?id=2820907 | |
- tag2vec http://ktsaurabh.weebly.com/uploads/3/1/7/8/31783965/distributed_representations_for_content-based_and_personalized_tag_recommendation.pdf | |
- category2vec http://www.anlp.jp/proceedings/annual_meeting/2015/pdf_dir/C4-3.pdf |