krosaen

#include <math.h>
#include <iostream>
#include <vector>

template <class Part>
struct PartScorer {
  struct PartAdapter {
    double PartArea(const Part& part) = delete;
  };

krosaen

def pretty_data(d):
    return '\n'.join(data_lines(d))


def data_lines(d, prefix='', last=True, add_bookends=True):
    def ground(d):
        return isinstance(d, (str, float, int)) or ground_list(d) or ground_dict(d)

    def ground_list(d):
        return isinstance(d, list) and len(d) < 6 and all([isinstance(el, (str, float, int)) for el in d])

krosaen

"""
Utilities for printing out a nice looking file tree, e.g

pretty_tree(('root', [
    ('a', [
        'file1.txt',
        'file2.txt',
    ]),
    ('b', [
        'file1.json',

krosaen

#!/usr/bin/python3

import argparse
import os
import subprocess
import sys


def main():
    parser = argparse.ArgumentParser('mirrors an s3 object to local root (~/local-data/s3)')

krosaen

def summarize_data_shape(example):
    """
    Given some (json serializeable) example, provide a concise summary of its structure
    by pruning it down to e.g one item per list.

    like https://github.com/krosaen/data-shapy/blob/master/data_shapy/data_shape.py
    but handles a best effort summary of class objects and tuples too
    """

    def is_ground(item):

krosaen

def count_kps_within_poly(polygon, keypoints):
    from matplotlib.collections import PolyCollection
    path = PolyCollection([polygon]).get_paths()[0]
    return np.sum(path.contains_points(keypoints))

assert 2 == count_kps_within_poly(
  [(0, 0), (100, 0), (200, 100), (100, 100), (0, 100)],
  [(50, 50), (200, 0), (1, 1)]
)

krosaen

import tensorflow as tf


def test_feed_independent_graph():
    """
    Example of a computation graph that depends on a placeholder.

    How can we feed in values from a queue? As it turns out, you need separate
    sess.run calls to deq and then feed the graph.
    """