| 1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180 |
216x
216x
260x
260x
260x
408x
128x
128x
408x
408x
408x
408x
13x
12x
12x
12x
7x
7x
2x
2x
7x
6x
4x
2x
4x
1x
1x
2x
1x
1x
1x
643x
643x
2882x
6x
6x
1x
5x
46x
46x
347x
347x
347x
948x
46x
30x
| /*
* Copyright (c) AXA Shared Services Spain S.A.
*
* Permission is hereby granted, free of charge, to any person obtaining
* a copy of this software and associated documentation files (the
* "Software"), to deal in the Software without restriction, including
* without limitation the rights to use, copy, modify, merge, publish,
* distribute, sublicense, and/or sell copies of the Software, and to
* permit persons to whom the Software is furnished to do so, subject to
* the following conditions:
*
* The above copyright notice and this permission notice shall be
* included in all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
* EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
* NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
* LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
* OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
* WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*/
/**
* Class for a generic classifier.
* This is an abstract class that must be implemented by subclasses that
* contains the real classifier algorithm.
*/
class Classifier {
/**
* Constructor of the class.
* Initialize the basic properties and structure of any classifier.
* @param {Object} settings Settings for initializing the instance.
*/
constructor(settings) {
this.settings = settings || {};
this.clear();
}
/**
* Clears the content of the instance.
* This is done by initializing the observations object, the labels array
* and the observation count.
*/
clear() {
this.observations = {};
this.labels = [];
this.observationCount = 0;
}
/**
* Adds a new label to the observation tree. If the label already exists,
* return the existing one without creating it again.
* @param {String} label Label to be created or getted.
* @returns {String[]} List of observations assigned to this label.
*/
addLabel(label) {
if (!this.observations[label]) {
this.observations[label] = [];
this.labels.push(label);
}
return this.observations[label];
}
/**
* Adds a new observation to the classifier.
* @param {String} observation Observation to be added.
* @param {String} label Label of the observation.
*/
addObservation(observation, label) {
const labelObservations = this.addLabel(label);
labelObservations.push(observation);
this.observationCount += 1;
}
/**
* Removes an observation from the observation list of a label.
* @param {String} observation Observation to be removed.
* @param {String} label Label where we want the observation to be removed.
*/
removeObservationByLabel(observation, label) {
if (this.observations[label]) {
const labelObservations = this.observations[label];
const index = labelObservations.indexOf(observation);
if (index !== -1) {
labelObservations.splice(index, 1);
if (labelObservations.length === 0) {
delete this.observations[label];
this.labels.splice(this.labels.indexOf(label), 1);
}
this.observationCount -= 1;
}
}
}
/**
* Removes an observation. The label of the observation can be passed or
* can be omitted. When omitted, it loops over all labels tryin to remove
* the given observation.
* @param {String} observation Observation to be removed.
* @param {String} label Label of the observation, or undefined to iterate over
* all labels.
*/
removeObservation(observation, label) {
if (label) {
this.removeObservationByLabel(observation, label);
} else {
for (let i = 0; i < this.labels.length; i += 1) {
this.removeObservationByLabel(observation, this.labels[i]);
}
}
}
/**
* Iterate all the observations to calculate the total observation count.
*/
recalculateObservationCount() {
let count = 0;
for (let i = 0, l = this.labels.length; i < l; i += 1) {
if (this.observations[this.labels[i]]) {
count += this.observations[this.labels[i]].length;
}
}
this.observationCount = count;
}
/**
* Classify one observation.
*/
classifyObservation() {
throw new Error(
'This method is not implemented. Must be implemented by child classes.'
);
}
/**
* Get all the labels and score for each label from one observation.
* @param {String} observation Observation to be classified.
* @returns {Object[]} Sorted array of classifications, that means label and the score.
*/
getClassifications(observation) {
const labels = [];
this.classifyObservation(observation, labels);
return labels.sort((x, y) => y.value - x.value);
}
/**
* Given an observation, get the label and score of the best classification.
* @param {String} observation Observation to be classified.
* @returns {Object} Best classification of the observation.
*/
getBestClassification(observation) {
const classifications = this.getClassifications(observation);
if (!classifications || classifications.length === 0) {
return undefined;
}
return classifications[0];
}
/**
* Creates a matrix filled with zeros, that relate every single observation
* with every single label.
* @returns {Number[][]} A bidimensional array where x is the observation
* and y is the label, filled to zeros.
*/
createClassificationMatrix() {
const result = [];
for (let i = 0; i < this.observationCount; i += 1) {
const classification = [];
result.push(classification);
for (let j = 0, l = this.labels.length; j < l; j += 1) {
classification.push(0);
}
}
return result;
}
}
module.exports = Classifier;
|