benman1

# # Recipe for creating a poster based on a video
#
# 1. download a video (for example, youtube-dl)
# 2. extract images from the video, say every second one frame (ffmpeg)
# 3. choose the images you want and delete the images you don't want
# 4. (this notebook) align the images horizontally and vertically based on the numbering


# got this part from https://note.nkmk.me/en/python-pillow-concat-images/
from PIL import Image

benman1

"""
This is based on keras-yolo3 (https://github.com/experiencor/keras-yolo3)
and licensed under MIT
"""
import os
import numpy as np
from tensorflow.keras.layers import Conv2D, Input, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D
from tensorflow.keras.layers import concatenate, add
from tensorflow.keras.models import Model
import struct

benman1

"""
A minimal implementation of Monte Carlo tree search (MCTS) in Python 3
Luke Harold Miles, July 2019, Public Domain Dedication
See also https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
https://gist.github.com/qpwo/c538c6f73727e254fdc7fab81024f6e1
"""
from abc import ABC, abstractmethod
from collections import defaultdict
import math

benman1

"""
Adapter from
[stackoverflow](https://stackoverflow.com/questions/39453948/create-a-html-page-with-a-list-of-packages-using-pdoc-module).

Please note that this script is currently limited to one level of submodules. Also it's a bit ugly. Help fix it, if you like.

This looks atrocious for markdown (my impresssion)
"""
from os import path, makedirs
import argparse

benman1

"""
Adapter from
[stackoverflow](https://stackoverflow.com/questions/39453948/create-a-html-page-with-a-list-of-packages-using-pdoc-module).

It's a bit ugly. Help fix it, if you like.
"""
from os import path, makedirs
import pdoc

benman1

#include <wn.h>
#include <stdlib.h>
#include <iostream>


// g++ wordnet_example.cpp /usr/lib/libwordnet.a -o wordnet_example
int main(int argc, char **argv) {
    if (wninit()) {
        printf("Fatal! Could not open the WordNet corpus!\n");
        exit(-1);

benman1

from numba import njit, jit, prange
import numpy as np
from numba.pycc import CC
from scipy.sparse import lil_matrix

cc = CC('adjacency_utils')

@cc.export('calc_dist', 'f8(f8[:], f8[:])')
@jit("f8(f8[:], f8[:])")
def calc_dist(u, v):

benman1

import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import FastICA
from sklearn.pipeline import make_pipeline
from sklearn.svm import SVC
from sklearn.base import TransformerMixin

class Reduce(TransformerMixin):
  def fit(self, X, y=None):

benman1

from wordcloud import (WordCloud, get_single_color_func)
import matplotlib.pyplot as plt
from colour import Color


class GroupedColorFunc(object):
    """Create a color function object which assigns DIFFERENT SHADES of
       specified colors to certain words based on the color to words mapping.

       Uses wordcloud.get_single_color_func

benman1

import numpy as np
import pandas as pd
import seaborn as sns
import scipy


def lift_chart(y_true, y_pred, bins=10, ax=None, normalize=False, labels=None):
    '''Given matched vectors of true versus predicted
    targets, plot them against each other.