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# coding: utf-8

import numpy as np
import numpy.linalg as ln

classes = ['Iris-setosa', 'Iris-versicolor', 'Iris-virginica']

def sigmoid(u):
  return 1. / (1. + np.e ** -u)

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time,user_id,source,conversion
1590863740,xobepz7opw,direct,0
1590863754,vpo60mcha1,facebook,0
1590864169,89u9knmqni,direct,0
1590864169,cdmgdvf6oo,google,0
1590864380,h0czqgvxbg,google,0
1590864409,cj98eurd91,google,0
1590864574,fqu9t0sd02,facebook,0
1590864646,89pp6xf2pb,google,0
1590864929,k1jtp0bz2j,facebook,0

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// ベース（合成先）画像のピクセル値を取得
UIImage *baseImage = [UIImage imageNamed:@"hand1-150x150.png"];
CGImageRef baseCGImage = baseImage.CGImage;
size_t baseBytesPerRow = CGImageGetBytesPerRow(baseCGImage);
CGDataProviderRef baseDataProvider = CGImageGetDataProvider(baseCGImage);
CFDataRef baseData = CGDataProviderCopyData(baseDataProvider);
UInt8 *basePixels = (UInt8*)CFDataGetBytePtr(baseData); // f*_p
    
// 合成結果のピクセル値の初期値をベース画像と同じものに設定
// これを反復法で書き換えていく

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#!/bin/bash

project_name=foo

project_files=(
  "config/"
  "scripts/"
  "queries/"
  "workflow1.dig"
  "workflow2.dig"

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# coding: utf-8

"""USAGE: %(program)s WIKI_XML_DUMP OUTPUT_PREFIX
"""

import logging
import os.path
import sys

import gensim.corpora.wikicorpus as wikicorpus

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Assume we have a binary classifier that gives the probability of being a positive sample in the [0.0, 1.0] range. Area Under the ROC Curve (AUC) quantitatively measures the accuracy of prediction made by such a classification model. Intuitively, what AUC does is to make sure if positive (i.e., label=1) samples in a validation set get higher probability of being positive than negative ones.

The AUC metric eventually gives a single value in [0.0, 1.0]. When we have five test samples sorted by their prediction results as follows, we can see that the classifier put higher probability to all positive samples, #1, #2, and #4, than the others. We define the best scenario as an AUC of 1.0.

Test sample #	Probability of label=1	True label
1	0.8	1
2	0.7	1
4	0.6	1
3	0.5	0

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Generic functions

• convert_label(const int|const float) - Convert from -1|1 to 0.0f|1.0f, or from 0.0f|1.0f to -1|1

• each_top_k(int K, Object group, double cmpKey, *) - Returns top-K values (or tail-K values when k is less than 0)

• generate_series(const int|bigint start, const int|bigint end) - Generate a series of values, from start to end. A similar function to PostgreSQL's generate_serics. http://www.postgresql.org/docs/current/static/functions-srf.html

  select generate_series(1,9);
  
  

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