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""" | |
(C) Mathieu Blondel - 2010 | |
License: BSD 3 clause | |
Implementation of the collapsed Gibbs sampler for | |
Latent Dirichlet Allocation, as described in | |
Finding scientifc topics (Griffiths and Steyvers) | |
""" |
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### MATPLOTLIBRC FORMAT | |
# This is a sample matplotlib configuration file - you can find a copy | |
# of it on your system in | |
# site-packages/matplotlib/mpl-data/matplotlibrc. If you edit it | |
# there, please note that it will be overridden in your next install. | |
# If you want to keep a permanent local copy that will not be | |
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux | |
# like systems) and C:\Documents and Settings\yourname\.matplotlib | |
# (win32 systems). |
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王 | |
江 | |
周 | |
胡 | |
刘 | |
李 | |
吴 | |
毛 | |
温 | |
习 |
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SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md)) | |
HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md)) | |
all : $(SLIDES) $(HANDOUTS) | |
%.md.slides.pdf : %.md | |
pandoc $^ -t beamer --slide-level 2 -o $@ | |
%.md.handout.pdf : %.md | |
pandoc $^ -t beamer --slide-level 2 -V handout -o $@ |
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package com.rishav.hbase.union; | |
import java.util.ArrayList; | |
import java.util.List; | |
import org.apache.hadoop.conf.Configuration; | |
import org.apache.hadoop.conf.Configured; | |
import org.apache.hadoop.hbase.client.Scan; | |
import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil; | |
import org.apache.hadoop.hbase.util.Bytes; |
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#!/usr/bin/env python | |
# -*- coding: utf-8 -*- | |
# This is a simplified implementation of the LSTM language model (by Graham Neubig) | |
# | |
# LSTM Neural Networks for Language Modeling | |
# Martin Sundermeyer, Ralf Schlüter, Hermann Ney | |
# InterSpeech 2012 | |
# | |
# The structure of the model is extremely simple. At every time step we |
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""" | |
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
BSD License | |
""" | |
import numpy as np | |
# data I/O | |
data = open('input.txt', 'r').read() # should be simple plain text file | |
chars = list(set(data)) | |
data_size, vocab_size = len(data), len(chars) |
These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.
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