wpm

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.*;

/**

wpm

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):

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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
 */

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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

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"""
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

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"""
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/
"""

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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:]

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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]:

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import json
from json import JSONDecodeError
from typing import Sequence

import click


class JSONList(click.ParamType):

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