jvangael

#!/usr/bin/env python
"""Reads json lines from stdin and write csv to stdout.

Usage:
  json2csv.py -f <field>...
  json2csv.py -h | --help
  json2csv.py --version

Options:
  -h --help     Show this screen.

stucchio

from numpy import *
from scipy.stats import beta


class BetaBandit(object):
    def __init__(self, num_options=2, prior=(1.0,1.0)):
        self.trials = zeros(shape=(num_options,), dtype=int)
        self.successes = zeros(shape=(num_options,), dtype=int)
        self.num_options = num_options
        self.prior = prior

smly

"""
An example to check the AUC score on a validation set for each 10 epochs.
I hope it will be helpful for optimizing number of epochs.
"""
# -*- coding: utf-8 -*-
import logging

from sklearn.metrics import roc_auc_score
from keras.callbacks import Callback

Hello1024

#!/bin/bash

# Run this on This AMI on AWS:
# https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-b36981d8

# You should get yourself a fully working GPU enabled tensorflow installation.

cd ~

# grab cuda 7.0

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

bagder

NOTE

This content has moved.

Please go to bagder/TRRprefs for the current incarnation of the docs, and please help us out polish and maintain this documentation!

miguelmota

package main

import (
	"fmt"

	"github.com/aws/aws-sdk-go/aws"
	"github.com/aws/aws-sdk-go/aws/session"
	"github.com/aws/aws-sdk-go/service/ssm"
)

jwilson8767

from concurrent.futures import as_completed, ProcessPoolExecutor
import geopandas as gpd
import pandas as pd
import numpy as np
from collections.abc import Sequence
from shapely import prepared

def sjoin(left_df, right_df,  op='intersects', how='inner', lsuffix='left', rsuffix='right', fail_crs_mismatch: bool = True, fail_missing_geometries: bool = False) -> gpd.GeoDataFrame:
    """Spatial join of two GeoDataFrames. GeoPandas sjoin with concurrency (split naively using df slicing).
    

joeddav

import openai

class ChatGPT:
    """ A very simple wrapper around OpenAI's ChatGPT API. Makes it easy to create custom messages & chat. """
    
    def __init__(self, model="gpt-3.5-turbo", completion_hparams=None):
        self.model = model
        self.completion_hparams = completion_hparams or {}
        self.history = []
        self._messages = []