AadityaJ

const _prop1 = Symbol('prop1');
class Test{
    constructor(prop1, prop2){
        this[_prop1] = prop1;
        this.prop2 = prop2;
    }
}
const obj = new Test(1,2);
for(keys in obj){
    console.log(keys); //doesn print _prop1 property

AadityaJ

Symbol.for('abc') === Symbol.for('abc'); //returns true

AadityaJ

const symbolX = new Symbol('X'); // throws TypeError

AadityaJ

Symbol() == Symbol(); //returns false
Symbol('test') == Symbol('test'); //returns false

AadityaJ

#include <iostream>
#include <set>
#include <map>
#include <vector>
#include <fstream>
#include <sstream>
#include <iterator>
#include <algorithm>

using namespace std;

AadityaJ

import numpy as np
from gensim import matutils
from gensim.models.ldamodel import LdaModel
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import CountVectorizer
from gensim.sklearn_integration.SklearnWrapperGensimLdaModel import SklearnWrapperLdaModel
## class to execute fit_predict. Will later on add to lda wrapper.
class dummy(SklearnWrapperLdaModel):
    def fit_predict(self,X):
        corpus=matutils.Sparse2Corpus(X)

AadityaJ

#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz>
# Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html
#
"""
scikit learn interface for gensim for easy use of gensim with scikit-learn
"""
import numpy as np

AadityaJ

"""
scikit learn interface for gensim for easy use of gensim with scikit-learn


"""
import numpy as np
class BaseClass(object):
    def __init__(self):
        """init"""
    def run(self):

AadityaJ

"""
scikit learn interface for gensim for easy use of gensim with scikit-learn


"""
class BaseClass(object):
    def __init__(self):
        """init"""
    def run(self):
        return 0

AadityaJ

"""
scikit learn interface for gensim for easy use of gensim with scikit-learn


"""
class BaseClass(object):
    def __init__(self):
        """init"""
    def run(self):
        return 0