csandman

Chakra React Select

UPDATE: I finally made an NPM package for this component! It is made with TypeScript, and now has a fully customizable styling system. It is also far ahead of this wrapper at this point. Check it out here if you'd like: https://github.com/csandman/chakra-react-select

In order to use this component, you can implement it and use it like you would normally use react-select. It should accept all of the props that the original takes, however customizing the theme or the components could break this implementation. There are also a few extra things you can do with this wrapper that pull from the chakra library.

• You can pass the size prop with either sm, md, or lg. These will reflect the sizes available on the Chakra <Input /> component (with the exception of xs because it's too small to work).
• In your options objects, you can add the key isFixed to emulate the exa

logandillard

logandillard

import math
import numpy as np
from sklearn.linear_model import Ridge


class LinearModelTree:
    def __init__(self, min_node_size, node_model_fit_func, min_split_improvement=0):
        self.min_node_size = min_node_size
        self.node_model_fit_func = node_model_fit_func
        self.min_split_improvement = min_split_improvement

asmaier

#
# Some constants
#
aws_profile = "your_profile"
aws_region = "your_region"
s3_bucket = "your_bucket"

# 
# Reading environment variables from aws credential file 
#

AustinRochford

cbwar

def sizeof_fmt(num, suffix='o'):
    """Readable file size

    :param num: Bytes value
    :type num: int
    :param suffix: Unit suffix (optionnal) default = o
    :type suffix: str
    :rtype: str
    """
    for unit in ['', 'k', 'M', 'G', 'T', 'P', 'E', 'Z']:

ca0v

// ts 3.6x
function debounce<T extends Function>(cb: T, wait = 20) {
    let h = 0;
    let callable = (...args: any) => {
        clearTimeout(h);
        h = setTimeout(() => cb(...args), wait);
    };
    return <T>(<any>callable);
}

dusenberrymw

Spark Tips & Tricks

Misc. Tips & Tricks

• If values are integers in [0, 255], Parquet will automatically compress to use 1 byte unsigned integers, thus decreasing the size of saved DataFrame by a factor of 8.
• Partition DataFrames to have evenly-distributed, ~128MB partition sizes (empirical finding). Always err on the higher side w.r.t. number of partitions.
• Pay particular attention to the number of partitions when using flatMap, especially if the following operation will result in high memory usage. The flatMap op usually results in a DataFrame with a [much] larger number of rows, yet the number of partitions will remain the same. Thus, if a subsequent op causes a large expansion of memory usage (i.e. converting a DataFrame of indices to a DataFrame of large Vectors), the memory usage per partition may become too high. In this case, it is beneficial to repartition the output of flatMap to a number of partitions that will safely allow for appropriate partition memory sizes, based upon the

rizky

#!/bin/sh
# Install brew
/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
# Apple hides old versions of stuff at https://developer.apple.com/download/more/
# Install the latest XCode (8.0).
#   We used to install the XCode Command Line Tools 7.3 here, but that would just upset the most recent versions of brew.
#   So we're going to install all our brew dependencies first, and then downgrade the tools. You can switch back after
#   you have installed caffe.
# Install CUDA toolkit 8.0 release candidate
#   Register and download from https://developer.nvidia.com/cuda-release-candidate-download

miodeqqq

# -*- coding: utf-8 -*-

import hashlib
import sys
import time

# Using: ./hash.py hashcode
# For example: ./hash.py 9743a66f914cc249efca164485a19c5c

def timing(f):