ravishchawla

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<Project DefaultTargets="Build" ToolsVersion="4.0" xmlns="http://schemas.microsoft.com/developer/msbuild/2003">

  <ImportGroup Label="PropertySheets"/>
  
  <!-- User Macros -->
  <PropertyGroup Label="UserMacros">
    <PCL_ROOT Condition="'$(Platform)'=='Win32'">$(ProgramFiles)\PCL 1.7.2</PCL_ROOT>
    <PCL_ROOT Condition="'$(Platform)'=='x64'">$(ProgramW6432)\PCL 1.7.2</PCL_ROOT>
  </PropertyGroup>

ravishchawla

Arrays

Search in a rotated sorted arrya

We do a binary search. For each iteration, there are two different sorted arrays, one on the left, and one on the right. Its just a matter of checking in which sorted array our element lies in. We iterate on that side.

int low = 0;
int high = nums.length-1;
int mid;

ravishchawla

Hough Transform

1. Create a grid of parameter values
2. Each point votes for a set of parameters, incrementing those values in grid
3. Find maximum or local maxima in grid

Good

• Robust to outliers: each point votes separately
• Fairly efficient (much faster than trying all sets of parameters)
• Provides multiple good fits

ravishchawla

import time;
import datetime;
import requests;
import json;
import os;
import os.path;
import time;

'''
Bot to post Pull Request changes to a Slack channel.

ravishchawla

import sys;

def print_progess(currentProgress, maxProgress, numberDashes, message = ""):
    progress = numberDashes * (currentProgress / maxProgress) + 1;
    dashes = "=" * int(progress) + "> "
    percentage = int(100 * currentProgress / maxProgress) + 1;
    if percentage > 100:
        percentage = 100;
    sys.stdout.write("\r " + "<" + message + "> " + dashes + "{0:.2f}".format(percentage) + "%");
    sys.stdout.flush();

ravishchawla

# coding: utf-8

# # Training a Word2Vec Model on the Reddit Comments Dataset
# 
# ### Ravish Chawla

# In[276]:

get_ipython().magic('matplotlib inline')

ravishchawla

'''
Read reviews from a JSON-formatted file into an array.
'''
lines = [];
num_pos = 0; num_neg = 0; num_total = 75000;

with open('data/review.json', 'r') as f:
    for line in f:
        if (len(lines) >= (num_total * 2)):
            break;

ravishchawla

'''
Clean each document by removing unnecesary characters and splitting by space.
'''
def clean_document(doco):
    punctuation = string.punctuation + '\n\n';
    punc_replace = ''.join([' ' for s in punctuation]);
    doco_clean = doco.replace('-', ' ');
    doco_alphas = re.sub(r'\W +', '', doco_clean)
    trans_table = str.maketrans(punctuation, punc_replace);
    doco_clean = ' '.join([word.translate(trans_table) for word in doco_alphas.split(' ')]);

ravishchawla

# Use a Keras Tokenizer and fit on the sentences

tokenizer = Tokenizer();
tokenizer.fit_on_texts(sentences);
text_sequences = np.array(tokenizer.texts_to_sequences(sentences));
sequence_dict = tokenizer.word_index;
word_dict = dict((num, val) for (val, num) in sequence_dict.items());

# We get a map of encoding-to-word in sequence_dict

ravishchawla

# Truncate and Pad reviews at a Maximum cap of 60 words.
max_cap = 60;
X = pad_sequences(reviews_encoded, maxlen=max_cap, truncating='post')

# Obtain a One-hot Y array for each review label.
Y = np.array([[0,1] if '0' in label else [1,0] for label in labels])

# Get a randomized sequence of positions to shuffle reviews
np.random.seed(1024);
random_posits = np.arange(len(X))