W.P. McNeill wpm

## ItemSet.java
package wpmcn.structure;

import org.apache.commons.lang.StringUtils;
import org.apache.hadoop.io.ArrayWritable;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.WritableComparable;

import java.util.*;

/**

## spark_parallel_boost.py
from pyspark import SparkContext

import numpy as np

from sklearn.cross_validation import train_test_split, Bootstrap
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier

def run(sc):

## poll.js
var Promise = require('bluebird');

/**
 * Periodically poll a signal function until either it returns true or a timeout is reached.
 *
 * @param signal function that returns true when the polled operation is complete
 * @param interval time interval between polls in milliseconds
 * @param timeout period of time before giving up on polling
 * @returns true if the signal function returned true, false if the operation timed out
 */

## multi_join.py
import pandas

"""
Join an arbitrary number of data frames, using a multi-index label for each data frame.

For example say you have three data frames each of which lists the classroom and
number of students a teacher has in a given period.

        Classroom  Students
Teacher

## simple_mnist.py
"""
A minimal implementation of the MNIST handwritten digits classification task in TensorFlow.

This runs MNIST images images through a single hidden layer and softmax loss function.

It demonstrates in a single Python source file the basics of creating a model, training and evaluating data sets, and
writing summaries that can be visualized by TensorBoard.
"""
from __future__ import division

## stanford_sentiment_to_csv.py
"""
Put all the Stanford Sentiment Treebank phrase data into test, training, and dev CSVs.

Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive Deep Models
for Semantic Compositionality Over a Sentiment Treebank. Presented at the Conference on Empirical Methods in Natural
Language Processing EMNLP.

https://nlp.stanford.edu/sentiment/
"""

## Entity Highlighting in Context.ipynb

      
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                wpm
                / Entity Highlighting in Context.ipynb
            
            
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              December 4, 2017 16:12
            
              
                Entity Highlighting in Context
              
          
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## spacy_paragraph_segmenter.py
def paragraphs(document):
    start = 0
    for token in document:
        if token.is_space and token.text.count("\n") > 1:
            yield document[start:token.i]
            start = token.i
    yield document[start:]

## spacy_pattern_match.py
import json
from json import JSONDecodeError
from typing import Sequence, Iterable, List

import click
import spacy
from spacy.matcher import Matcher


def match_patterns(nlp, patterns: Sequence[dict], corpus: Iterable[str]) -> Iterable[str]:

## json_to_jsonl.py
import json
from json import JSONDecodeError
from typing import Sequence

import click


class JSONList(click.ParamType):

    def convert(self, value: str, _, __) -> Sequence:
	package wpmcn.structure;

	import org.apache.commons.lang.StringUtils;
	import org.apache.hadoop.io.ArrayWritable;
	import org.apache.hadoop.io.Text;
	import org.apache.hadoop.io.WritableComparable;

	import java.util.*;

	/**
	from pyspark import SparkContext

	import numpy as np

	from sklearn.cross_validation import train_test_split, Bootstrap
	from sklearn.datasets import make_classification
	from sklearn.metrics import accuracy_score
	from sklearn.tree import DecisionTreeClassifier

	def run(sc):
	var Promise = require('bluebird');

	/**
	* Periodically poll a signal function until either it returns true or a timeout is reached.
	*
	* @param signal function that returns true when the polled operation is complete
	* @param interval time interval between polls in milliseconds
	* @param timeout period of time before giving up on polling
	* @returns true if the signal function returned true, false if the operation timed out
	*/
	import pandas

	"""
	Join an arbitrary number of data frames, using a multi-index label for each data frame.

	For example say you have three data frames each of which lists the classroom and
	number of students a teacher has in a given period.

	Classroom Students
	Teacher
	"""
	A minimal implementation of the MNIST handwritten digits classification task in TensorFlow.

	This runs MNIST images images through a single hidden layer and softmax loss function.

	It demonstrates in a single Python source file the basics of creating a model, training and evaluating data sets, and
	writing summaries that can be visualized by TensorBoard.
	"""
	from __future__ import division
	"""
	Put all the Stanford Sentiment Treebank phrase data into test, training, and dev CSVs.

	Socher, R., Perelygin, A., Wu, J. Y., Chuang, J., Manning, C. D., Ng, A. Y., & Potts, C. (2013). Recursive Deep Models
	for Semantic Compositionality Over a Sentiment Treebank. Presented at the Conference on Empirical Methods in Natural
	Language Processing EMNLP.

	https://nlp.stanford.edu/sentiment/
	"""
	def paragraphs(document):
	start = 0
	for token in document:
	if token.is_space and token.text.count("\n") > 1:
	yield document[start:token.i]
	start = token.i
	yield document[start:]
	import json
	from json import JSONDecodeError
	from typing import Sequence, Iterable, List

	import click
	import spacy
	from spacy.matcher import Matcher


	def match_patterns(nlp, patterns: Sequence[dict], corpus: Iterable[str]) -> Iterable[str]:
	import json
	from json import JSONDecodeError
	from typing import Sequence

	import click


	class JSONList(click.ParamType):

	def convert(self, value: str, _, __) -> Sequence: