mattlewissf

git add -p is your friend

git add -p is basically "git add partial (or patch)"

Patch mode allows you to stage parts of a changed file, instead of the entire file. This allows you to make concise, well-crafted commits that make for an easier to read history. This feature can improve the quality of the commits. It also makes it easy to remove parts of the changes in a file that were only there for debugging purposes - prior to the commit without having to go back to the editor.

It allows you to see the changes (delta) to the code that you are trying to add, and lets you add them (or not) separately from each other using an interactive prompt. Here's how to use it:

from the command line, either use

• git add -p

mattlewissf

---
title:  "Hey, what a weird way to set titles"
date:   2017-05-24 15:00:00
categories:
tags:
---

I'm gonna try some stuff!

<div id="chart"></div>

mattlewissf

<style>

html {
  font-size: 62.5% !important; }
body {
  font-size: 1.8em !important; /* currently ems cause chrome bug misinterpreting rems on body element */
  line-height: 1.6 !important;
  font-weight: 200 !important;
  color: #222 !important; }

mattlewissf

<style>

html {
  font-size: 62.5% !important; }
body {
  font-size: 1.5em !important; /* currently ems cause chrome bug misinterpreting rems on body element */
  line-height: 1.6 !important;
  font-weight: 400 !important;
  font-family: "Raleway", "HelveticaNeue", "Helvetica Neue", Helvetica, Arial, sans-serif !important;
  color: #222 !important; }

mattlewissf

# written and discarded in favor of sqlalchemy methods - still a good example of how to dynamically create classes in Python

import glob
import sys
from sqlalchemy import Column, ForeignKey, Integer, String
from sqlalchemy.dialects.mysql import TIMESTAMP
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import relationship
from sqlalchemy import create_engine
import pandas as pd

mattlewissf

""" First attempt at Kaggle Digit Recognizer | https://www.kaggle.com/c/digit-recognizer/
    Basic approach w/ random trees results in 0.96486 accuracy
"""


import pandas as pd
import numpy as np
from numpy import savetxt
from sklearn.ensemble import RandomForestClassifier

mattlewissf

import unittest

def gap(g,m,n):
    primes = []

    # finds primes in range
    for num in xrange(m,n+1,1): # n to be included in range
        x_half = num / 2
        is_prime = all(num % x != 0 for x in xrange(2,x_half+1,1))
        if is_prime == True:

mattlewissf

lambda x: -683.923549636
lambda x: -116.783533207 * (-(x[6] - -5.1676818541) if x[6] <= -5.41238626298 else 0 if x[6] >= -4.67412019866 else (-0.007619371398 * (x[6] - -4.67412019866) ** 2 + -0.61845994042 * (x[6] - -4.67412019866) ** 3))
lambda x: -29.1588158614 * (0 if x[6] <= -0.137738977403 else (x[6] - 0.751892264604) if x[6] >= 1.25162817331 else (0.0569021591939 * (x[6] - -0.137738977403) ** 2 + 0.145377420148 * (x[6] - -0.137738977403) ** 3)) * (0 if x[6] <= -5.41238626298 else (x[6] - -5.1676818541) if x[6] >= -4.67412019866 else (1.36214458707 * (x[6] - -5.41238626298) ** 2 + -0.61845994042 * (x[6] - -5.41238626298) ** 3))
lambda x: 28.2353765603 * (-(x[6] - 0.751892264604) if x[6] <= -0.137738977403 else 0 if x[6] >= 1.25162817331 else (0.662849995222 * (x[6] - 1.25162817331) ** 2 + 0.145377420148 * (x[6] - 1.25162817331) ** 3)) * (0 if x[6] <= -5.41238626298 else (x[6] - -5.1676818541) if x[6] >= -4.67412019866 else (1.36214458707 * (x[6] - -5.41238626298) ** 2 + -0.61845994042 * (x[6] - -5.412386

mattlewissf

class BoggleBoard
 
  def initialize(board)
    @board = board
  end
 
  def create_word(*coords)
    coords.map { |coord| @board[coord.first][coord.last]}.join("")
  end
  

mattlewissf

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