2 * Copyright (c) 2016 Samsung Electronics Co., Ltd.
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
8 * http://www.apache.org/licenses/LICENSE-2.0
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
17 #include "VisitCateger.h"
20 #include "ProbFeaturesModel.h"
23 // categorizer model parameters trained offline (implemented in python):
24 const std::map<int, ctx::MahalModel> ctx::VisitCateger::__models({
29 0.588408417316, 0.475154840361, -0.564085141865, 0.077711893790, 0.782630398597, -0.650926403250, -0.101943950378
32 0.797977863370, -0.133179879257, 0.242803062646, -0.059448581046, -0.117039646748, 0.097645535057, 0.014250290052,
33 -0.133179879257, 1.028900241400, -0.252245407243, 0.058455883835, -0.283950879851, 0.172204076583, 0.247906065767,
34 0.242803062646, -0.252245407243, 1.832134785177, 0.327188225606, 0.874905851092, -0.371088938012, -1.289816938938,
35 -0.059448581046, 0.058455883835, 0.327188225606, 1.455090164348, 0.138363721074, 0.216985279422, -1.113021128017,
36 -0.117039646748, -0.283950879851, 0.874905851092, 0.138363721074, 1.379674755873, -0.977922749615, -0.738895486376,
37 0.097645535057, 0.172204076583, -0.371088938012, 0.216985279422, -0.977922749615, 0.899928922718, -0.158101631251,
38 0.014250290052, 0.247906065767, -1.289816938938, -1.113021128017, -0.738895486376, -0.158101631251, 2.644105309746
45 -0.128092670982, -0.762177819157, 0.262924477521, -0.412038966097, -1.049141893517, 1.104760800499, -0.628939955525
48 15.751249839350, 11.389025401325, -9.885346240379, -0.010809392387, -1.308837060762, 0.970778241189, 0.558946631235,
49 11.389025401325, 12.830223040140, -8.517695939156, 0.293693134532, -0.845784968295, 1.418175236596, -2.246658259974,
50 -9.885346240379, -8.517695939156, 10.222750966685, 0.390448668966, 1.095218945062, -0.403733435617, -1.815103304859,
51 -0.010809392387, 0.293693134532, 0.390448668966, 2.256864603458, 0.632080300647, -0.019551779384, -1.751417951792,
52 -1.308837060762, -0.845784968295, 1.095218945062, 0.632080300647, 3.132753467561, -1.427748733399, -4.291958669471,
53 0.970778241189, 1.418175236596, -0.403733435617, -0.019551779384, -1.427748733399, 1.183055586213, 0.200571452172,
54 0.558946631235, -2.246658259974, -1.815103304859, -1.751417951792, -4.291958669471, 0.200571452172, 11.668888615934
61 -0.542340098504, 0.184789511765, 0.387451546413, 0.301902661472, 0.109392397093, -0.310468874039, 0.709513920221
64 2.153884992301, -0.129488409324, 0.136236052776, -0.138043678532, -0.227492557156, 0.117810812390, 0.265072329266,
65 -0.129488409324, 3.165213522741, -1.751520714507, 0.467831090302, -0.483916138161, 0.376293684450, 0.149387541935,
66 0.136236052776, -1.751520714507, 2.483475248800, 0.384085303028, 0.338642175318, -0.052000492068, -0.801404345627,
67 -0.138043678532, 0.467831090302, 0.384085303028, 1.972390458477, -0.025332052563, 0.393845805027, -1.225948397955,
68 -0.227492557156, -0.483916138161, 0.338642175318, -0.025332052563, 0.890301343360, -0.549163112351, -0.746838701215,
69 0.117810812390, 0.376293684450, -0.052000492068, 0.393845805027, -0.549163112351, 0.474674836872, 0.012417969474,
70 0.265072329266, 0.149387541935, -0.801404345627, -1.225948397955, -0.746838701215, 0.012417969474, 2.104629121515
74 ctx::PiecewiseLin ctx::VisitCateger::__chiApprox(
76 0. , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08,
77 0.09, 0.1 , 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17,
78 0.18, 0.19, 0.2 , 0.21, 0.22, 0.23, 0.24, 0.25, 0.26,
79 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32, 0.33, 0.34, 0.35,
80 0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43, 0.44,
81 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 , 0.51, 0.52, 0.53,
82 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 , 0.61, 0.62,
83 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 , 0.71,
84 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ,
85 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89,
86 0.9 , 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98,
87 0.99, 1. , 1.01, 1.02, 1.03, 1.04, 1.05, 1.06, 1.07,
88 1.08, 1.09, 1.1 , 1.11, 1.12, 1.13, 1.14, 1.15, 1.16,
89 1.17, 1.18, 1.19, 1.2 , 1.21, 1.22, 1.23, 1.24, 1.25,
90 1.26, 1.27, 1.28, 1.29, 1.3 , 1.31, 1.32, 1.33, 1.34,
91 1.35, 1.36, 1.37, 1.38, 1.39, 1.4 , 1.41, 1.42, 1.43,
92 1.44, 1.45, 1.46, 1.47, 1.48, 1.49, 1.5 , 1.51, 1.52,
93 1.53, 1.54, 1.55, 1.56, 1.57, 1.58, 1.59, 1.6 , 1.61,
94 1.62, 1.63, 1.64, 1.65, 1.66, 1.67, 1.68, 1.69, 1.7 ,
95 1.71, 1.72, 1.73, 1.74, 1.75, 1.76, 1.77, 1.78, 1.79,
96 1.8 , 1.81, 1.82, 1.83, 1.84, 1.85, 1.86, 1.87, 1.88,
97 1.89, 1.9 , 1.91, 1.92, 1.93, 1.94, 1.95, 1.96, 1.97,
98 1.98, 1.99, 2. , 2.01, 2.02, 2.03, 2.04, 2.05, 2.06,
99 2.07, 2.08, 2.09, 2.1 , 2.11, 2.12, 2.13, 2.14, 2.15,
100 2.16, 2.17, 2.18, 2.19, 2.2 , 2.21, 2.22, 2.23, 2.24,
101 2.25, 2.26, 2.27, 2.28, 2.29, 2.3 , 2.31, 2.32, 2.33,
102 2.34, 2.35, 2.36, 2.37, 2.38, 2.39, 2.4 , 2.41, 2.42,
103 2.43, 2.44, 2.45, 2.46, 2.47, 2.48, 2.49, 2.5 , 2.51,
104 2.52, 2.53, 2.54, 2.55, 2.56, 2.57, 2.58, 2.59, 2.6 ,
105 2.61, 2.62, 2.63, 2.64, 2.65, 2.66, 2.67, 2.68, 2.69,
106 2.7 , 2.71, 2.72, 2.73, 2.74, 2.75, 2.76, 2.77, 2.78,
107 2.79, 2.8 , 2.81, 2.82, 2.83, 2.84, 2.85, 2.86, 2.87,
108 2.88, 2.89, 2.9 , 2.91, 2.92, 2.93, 2.94, 2.95, 2.96,
109 2.97, 2.98, 2.99, 3. , 3.01, 3.02, 3.03, 3.04, 3.05,
110 3.06, 3.07, 3.08, 3.09, 3.1 , 3.11, 3.12, 3.13, 3.14,
111 3.15, 3.16, 3.17, 3.18, 3.19, 3.2 , 3.21, 3.22, 3.23,
112 3.24, 3.25, 3.26, 3.27, 3.28, 3.29, 3.3 , 3.31, 3.32,
113 3.33, 3.34, 3.35, 3.36, 3.37, 3.38, 3.39, 3.4 , 3.41,
114 3.42, 3.43, 3.44, 3.45, 3.46, 3.47, 3.48, 3.49, 3.5 ,
115 3.51, 3.52, 3.53, 3.54, 3.55, 3.56, 3.57, 3.58, 3.59,
116 3.6 , 3.61, 3.62, 3.63, 3.64, 3.65, 3.66, 3.67, 3.68,
117 3.69, 3.7 , 3.71, 3.72, 3.73, 3.74, 3.75, 3.76, 3.77,
118 3.78, 3.79, 3.8 , 3.81, 3.82, 3.83, 3.84, 3.85, 3.86,
119 3.87, 3.88, 3.89, 3.9 , 3.91, 3.92, 3.93, 3.94, 3.95,
120 3.96, 3.97, 3.98, 3.99, 4. , 4.01, 4.02, 4.03, 4.04,
121 4.05, 4.06, 4.07, 4.08, 4.09, 4.1 , 4.11, 4.12, 4.13,
122 4.14, 4.15, 4.16, 4.17, 4.18, 4.19, 4.2 , 4.21, 4.22,
123 4.23, 4.24, 4.25, 4.26, 4.27, 4.28, 4.29, 4.3 , 4.31,
124 4.32, 4.33, 4.34, 4.35, 4.36, 4.37, 4.38, 4.39, 4.4 ,
125 4.41, 4.42, 4.43, 4.44, 4.45, 4.46, 4.47, 4.48, 4.49,
126 4.5 , 4.51, 4.52, 4.53, 4.54, 4.55, 4.56, 4.57, 4.58,
127 4.59, 4.6 , 4.61, 4.62, 4.63, 4.64, 4.65, 4.66, 4.67,
128 4.68, 4.69, 4.7 , 4.71, 4.72, 4.73, 4.74, 4.75, 4.76,
129 4.77, 4.78, 4.79, 4.8 , 4.81, 4.82, 4.83, 4.84, 4.85,
130 4.86, 4.87, 4.88, 4.89, 4.9 , 4.91, 4.92, 4.93, 4.94,
131 4.95, 4.96, 4.97, 4.98, 4.99, 5. , 5.01, 5.02, 5.03,
132 5.04, 5.05, 5.06, 5.07, 5.08, 5.09, 5.1 , 5.11, 5.12,
133 5.13, 5.14, 5.15, 5.16, 5.17, 5.18, 5.19, 5.2 , 5.21,
134 5.22, 5.23, 5.24, 5.25, 5.26, 5.27, 5.28, 5.29, 5.3 ,
135 5.31, 5.32, 5.33, 5.34, 5.35, 5.36, 5.37, 5.38, 5.39,
136 5.4 , 5.41, 5.42, 5.43, 5.44, 5.45, 5.46, 5.47, 5.48,
137 5.49, 5.5 , 5.51, 5.52, 5.53, 5.54, 5.55, 5.56, 5.57,
138 5.58, 5.59, 5.6 , 5.61, 5.62, 5.63, 5.64, 5.65, 5.66,
139 5.67, 5.68, 5.69, 5.7 , 5.71, 5.72, 5.73, 5.74, 5.75,
140 5.76, 5.77, 5.78, 5.79, 5.8 , 5.81, 5.82, 5.83, 5.84,
141 5.85, 5.86, 5.87, 5.88, 5.89, 5.9 , 5.91, 5.92, 5.93,
142 5.94, 5.95, 5.96, 5.97, 5.98, 5.99, 6. , 6.01, 6.02,
143 6.03, 6.04, 6.05, 6.06, 6.07, 6.08, 6.09, 6.1 , 6.11,
144 6.12, 6.13, 6.14, 6.15, 6.16, 6.17, 6.18, 6.19, 6.2 ,
145 6.21, 6.22, 6.23, 6.24, 6.25, 6.26, 6.27, 6.28, 6.29,
146 6.3 , 6.31, 6.32, 6.33, 6.34, 6.35, 6.36, 6.37, 6.38,
147 6.39, 6.4 , 6.41, 6.42, 6.43, 6.44, 6.45, 6.46, 6.47,
148 6.48, 6.49, 6.5 , 6.51, 6.52, 6.53, 6.54, 6.55, 6.56,
149 6.57, 6.58, 6.59, 6.6 , 6.61, 6.62, 6.63, 6.64, 6.65,
150 6.66, 6.67, 6.68, 6.69, 6.7 , 6.71, 6.72, 6.73, 6.74,
151 6.75, 6.76, 6.77, 6.78, 6.79, 6.8 , 6.81, 6.82, 6.83,
152 6.84, 6.85, 6.86, 6.87, 6.88, 6.89, 6.9 , 6.91, 6.92,
153 6.93, 6.94, 6.95, 6.96, 6.97, 6.98, 6.99, 7.
156 0. , 0. , 0. , 0. , 0. , 0. ,
157 0. , 0. , 0. , 0. , 0. , 0. ,
158 0. , 0. , 0. , 0. , 0. , 0. ,
159 0. , 0. , 0. , 0. , 0. , 0. ,
160 0. , 0. , 0.000001, 0.000001, 0.000001, 0.000001,
161 0.000002, 0.000002, 0.000003, 0.000003, 0.000004, 0.000005,
162 0.000006, 0.000007, 0.000008, 0.00001 , 0.000012, 0.000014,
163 0.000016, 0.000019, 0.000023, 0.000026, 0.000031, 0.000035,
164 0.000041, 0.000047, 0.000054, 0.000062, 0.00007 , 0.00008 ,
165 0.000091, 0.000103, 0.000116, 0.000131, 0.000147, 0.000165,
166 0.000185, 0.000207, 0.000231, 0.000257, 0.000285, 0.000316,
167 0.00035 , 0.000387, 0.000427, 0.000471, 0.000518, 0.000569,
168 0.000624, 0.000683, 0.000747, 0.000816, 0.00089 , 0.00097 ,
169 0.001055, 0.001147, 0.001245, 0.001349, 0.001461, 0.00158 ,
170 0.001708, 0.001843, 0.001987, 0.002141, 0.002303, 0.002476,
171 0.002659, 0.002854, 0.003059, 0.003276, 0.003506, 0.003748,
172 0.004004, 0.004274, 0.004558, 0.004857, 0.005171, 0.005502,
173 0.00585 , 0.006215, 0.006597, 0.006999, 0.007419, 0.007859,
174 0.00832 , 0.008802, 0.009305, 0.009831, 0.01038 , 0.010953,
175 0.01155 , 0.012172, 0.01282 , 0.013495, 0.014197, 0.014927,
176 0.015686, 0.016473, 0.017291, 0.01814 , 0.019021, 0.019933,
177 0.020879, 0.021858, 0.022872, 0.023921, 0.025006, 0.026127,
178 0.027285, 0.028482, 0.029717, 0.030992, 0.032306, 0.033662,
179 0.035059, 0.036497, 0.037979, 0.039504, 0.041073, 0.042687,
180 0.044345, 0.04605 , 0.047801, 0.049599, 0.051445, 0.053339,
181 0.055282, 0.057273, 0.059315, 0.061406, 0.063548, 0.065742,
182 0.067986, 0.070283, 0.072632, 0.075034, 0.077488, 0.079996,
183 0.082558, 0.085174, 0.087843, 0.090568, 0.093346, 0.09618 ,
184 0.099069, 0.102013, 0.105012, 0.108066, 0.111176, 0.114342,
185 0.117563, 0.120839, 0.124171, 0.127558, 0.131001, 0.134499,
186 0.138052, 0.141659, 0.145322, 0.149039, 0.15281 , 0.156635,
187 0.160514, 0.164446, 0.168431, 0.172469, 0.176559, 0.180701,
188 0.184894, 0.189138, 0.193432, 0.197776, 0.202169, 0.206611,
189 0.211101, 0.215639, 0.220223, 0.224853, 0.229528, 0.234248,
190 0.239012, 0.24382 , 0.248669, 0.25356 , 0.258492, 0.263464,
191 0.268474, 0.273523, 0.278608, 0.283731, 0.288888, 0.29408 ,
192 0.299305, 0.304562, 0.309851, 0.31517 , 0.320519, 0.325895,
193 0.331299, 0.336729, 0.342185, 0.347664, 0.353166, 0.35869 ,
194 0.364234, 0.369798, 0.375381, 0.38098 , 0.386596, 0.392227,
195 0.397871, 0.403529, 0.409197, 0.414876, 0.420565, 0.426261,
196 0.431964, 0.437673, 0.443387, 0.449103, 0.454822, 0.460543,
197 0.466263, 0.471981, 0.477698, 0.483411, 0.48912 , 0.494822,
198 0.500518, 0.506206, 0.511886, 0.517554, 0.523212, 0.528858,
199 0.53449 , 0.540108, 0.545711, 0.551297, 0.556866, 0.562417,
200 0.567948, 0.573459, 0.578949, 0.584417, 0.589861, 0.595282,
201 0.600677, 0.606048, 0.611391, 0.616707, 0.621995, 0.627254,
202 0.632483, 0.637681, 0.642848, 0.647984, 0.653086, 0.658155,
203 0.66319 , 0.66819 , 0.673155, 0.678084, 0.682976, 0.687831,
204 0.692649, 0.697428, 0.702168, 0.70687 , 0.711531, 0.716153,
205 0.720733, 0.725273, 0.729772, 0.734228, 0.738643, 0.743015,
206 0.747344, 0.75163 , 0.755873, 0.760072, 0.764227, 0.768339,
207 0.772406, 0.776428, 0.780406, 0.784339, 0.788228, 0.792071,
208 0.795869, 0.799622, 0.80333 , 0.806992, 0.810609, 0.814181,
209 0.817707, 0.821188, 0.824624, 0.828015, 0.83136 , 0.83466 ,
210 0.837916, 0.841126, 0.844292, 0.847413, 0.85049 , 0.853522,
211 0.85651 , 0.859455, 0.862355, 0.865212, 0.868026, 0.870796,
212 0.873524, 0.876209, 0.878852, 0.881452, 0.884011, 0.886528,
213 0.889005, 0.89144 , 0.893834, 0.896189, 0.898503, 0.900778,
214 0.903014, 0.90521 , 0.907369, 0.909488, 0.911571, 0.913615,
215 0.915623, 0.917594, 0.919528, 0.921427, 0.92329 , 0.925118,
216 0.926911, 0.92867 , 0.930395, 0.932086, 0.933745, 0.93537 ,
217 0.936963, 0.938525, 0.940055, 0.941554, 0.943022, 0.94446 ,
218 0.945869, 0.947248, 0.948598, 0.949919, 0.951213, 0.952478,
219 0.953717, 0.954928, 0.956113, 0.957273, 0.958406, 0.959514,
220 0.960598, 0.961657, 0.962692, 0.963703, 0.964692, 0.965657,
221 0.9666 , 0.967521, 0.968421, 0.969299, 0.970156, 0.970993,
222 0.97181 , 0.972607, 0.973385, 0.974144, 0.974884, 0.975605,
223 0.976309, 0.976996, 0.977665, 0.978317, 0.978953, 0.979572,
224 0.980176, 0.980764, 0.981337, 0.981895, 0.982438, 0.982967,
225 0.983482, 0.983984, 0.984472, 0.984947, 0.985409, 0.985858,
226 0.986296, 0.986721, 0.987135, 0.987537, 0.987929, 0.988309,
227 0.988678, 0.989038, 0.989387, 0.989726, 0.990056, 0.990376,
228 0.990687, 0.990989, 0.991282, 0.991566, 0.991843, 0.992111,
229 0.992371, 0.992624, 0.992869, 0.993106, 0.993337, 0.99356 ,
230 0.993777, 0.993988, 0.994191, 0.994389, 0.99458 , 0.994766,
231 0.994946, 0.99512 , 0.995289, 0.995452, 0.99561 , 0.995764,
232 0.995912, 0.996056, 0.996195, 0.996329, 0.99646 , 0.996586,
233 0.996708, 0.996825, 0.996939, 0.99705 , 0.997156, 0.99726 ,
234 0.997359, 0.997456, 0.997549, 0.997639, 0.997726, 0.99781 ,
235 0.997891, 0.997969, 0.998045, 0.998118, 0.998189, 0.998257,
236 0.998323, 0.998386, 0.998447, 0.998506, 0.998563, 0.998618,
237 0.998671, 0.998723, 0.998772, 0.998819, 0.998865, 0.998909,
238 0.998952, 0.998993, 0.999033, 0.999071, 0.999107, 0.999143,
239 0.999177, 0.99921 , 0.999241, 0.999272, 0.999301, 0.999329,
240 0.999356, 0.999382, 0.999407, 0.999431, 0.999455, 0.999477,
241 0.999498, 0.999519, 0.999539, 0.999558, 0.999576, 0.999594,
242 0.999611, 0.999627, 0.999643, 0.999658, 0.999672, 0.999686,
243 0.999699, 0.999712, 0.999724, 0.999736, 0.999747, 0.999758,
244 0.999769, 0.999779, 0.999788, 0.999797, 0.999806, 0.999815,
245 0.999823, 0.99983 , 0.999838, 0.999845, 0.999852, 0.999858,
246 0.999865, 0.999871, 0.999876, 0.999882, 0.999887, 0.999892,
247 0.999897, 0.999902, 0.999906, 0.99991 , 0.999915, 0.999918,
248 0.999922, 0.999926, 0.999929, 0.999932, 0.999936, 0.999938,
249 0.999941, 0.999944, 0.999947, 0.999949, 0.999952, 0.999954,
250 0.999956, 0.999958, 0.99996 , 0.999962, 0.999964, 0.999965,
251 0.999967, 0.999969, 0.99997 , 0.999972, 0.999973, 0.999974,
252 0.999975, 0.999977, 0.999978, 0.999979, 0.99998 , 0.999981,
253 0.999982, 0.999983, 0.999984, 0.999984, 0.999985, 0.999986,
254 0.999987, 0.999987, 0.999988, 0.999988, 0.999989, 0.99999 ,
255 0.99999 , 0.999991, 0.999991, 0.999992, 0.999992, 0.999992,
256 0.999993, 0.999993, 0.999993, 0.999994, 0.999994, 0.999994,
257 0.999995, 0.999995, 0.999995, 0.999995, 0.999996, 0.999996,
258 0.999996, 0.999996, 0.999997, 0.999997, 0.999997, 0.999997,
259 0.999997, 0.999997, 0.999998, 0.999998, 0.999998, 0.999998,
260 0.999998, 0.999998, 0.999998, 0.999998, 0.999998, 0.999998,
261 0.999999, 0.999999, 0.999999, 0.999999, 0.999999, 0.999999,
262 0.999999, 0.999999, 0.999999, 0.999999, 0.999999, 0.999999,
263 0.999999, 0.999999, 0.999999, 0.999999, 0.999999, 0.999999,
264 0.999999, 1. , 1. , 1. , 1. , 1. ,
265 1. , 1. , 1. , 1. , 1. , 1. ,
266 1. , 1. , 1. , 1. , 1. , 1. ,
267 1. , 1. , 1. , 1. , 1. , 1. ,
268 1. , 1. , 1. , 1. , 1. , 1. ,
269 1. , 1. , 1. , 1. , 1. , 1. ,
270 1. , 1. , 1. , 1. , 1. , 1. ,
271 1. , 1. , 1. , 1. , 1. , 1. ,
272 1. , 1. , 1. , 1. , 1.
273 } // this is chi cdf for 7 degrees of freedom (because we have 7 features)
276 const std::vector<ctx::num_t> ctx::VisitCateger::__featuresMean(
287 const std::vector<ctx::num_t> ctx::VisitCateger::__featuresStd(
298 ctx::TimeFeatures ctx::VisitCateger::__timeFeatures(const time_t &time)
303 result = localtime_r(&time, &timeinfo);
305 return {0, 0, 0, false};
306 int minutesSinceMidnight = 60 * timeinfo.tm_hour + timeinfo.tm_min;
307 int weekday = (timeinfo.tm_wday + 6) % 7; // Monday is 0, Sunday is 6
308 bool weekend = weekday > 4;
309 int minutesSinceBeginingOfTheWeek = 24 * 60 * weekday + minutesSinceMidnight;
311 minutesSinceMidnight,
312 minutesSinceBeginingOfTheWeek,
318 int ctx::VisitCateger::__weeksScope(const TimeFeatures &startF, const Interval &interval)
320 int durationMinutes = (interval.end - interval.start) / 60;
321 int scopeMinutes = startF.minutesSinceBeginingOfTheWeek + durationMinutes;
322 int weeksScope = scopeMinutes / __MINUTES_IN_WEEK;
323 if (scopeMinutes % __MINUTES_IN_WEEK > 0)
328 ctx::num_t ctx::VisitCateger::__sum(const std::vector<num_t> model, const size_t &from, const size_t &to)
330 size_t toSecure = to;
331 if (to >= model.size()) {
332 _E("'to' exceeds model size");
333 toSecure = model.size() - 1;
336 _E("'from' greater than 'to'");
338 for (size_t i = from; i <= toSecure; i++) {
344 ctx::num_t ctx::VisitCateger::__weekModelMeanValue(PlaceCategId categ, const Interval &interval,
345 const TimeFeatures &startF, const TimeFeatures &endF)
349 int ws = __weeksScope(startF, interval);
350 for (int week = 1; week <= ws; week++) {
351 size_t startIndex = (week == 1)
352 ? startF.minutesSinceBeginingOfTheWeek
354 size_t endIndex = (week == ws)
355 ? endF.minutesSinceBeginingOfTheWeek
356 : __MINUTES_IN_WEEK - 1;
357 ret += __sum(prob_features::weekModel[categ], startIndex, endIndex);
358 minutes += endIndex - startIndex + 1;
361 return ret / minutes;
365 ctx::Categs ctx::VisitCateger::__weekModelFeatures(const Interval &interval, const TimeFeatures &startF, const TimeFeatures &endF)
368 for (const auto &item : prob_features::weekModel) {
369 categs[item.first] = __weekModelMeanValue(item.first, interval, startF, endF);
371 _D("categs: H=%.12f, W=%.12f, O=%.12f",
372 categs[PLACE_CATEG_ID_HOME],
373 categs[PLACE_CATEG_ID_WORK],
374 categs[PLACE_CATEG_ID_OTHER]);
378 ctx::IntervalFeatures ctx::VisitCateger::__intervalFeatures(const Interval &interval)
380 num_t durationMinutes = 1.0 * (interval.end - interval.start) / 60;
381 TimeFeatures startFeatures = __timeFeatures(interval.start);
382 TimeFeatures endFeatures = __timeFeatures(interval.end);
383 Categs weekFeatures = __weekModelFeatures(interval, startFeatures, endFeatures);
386 (num_t) startFeatures.minutesSinceMidnight,
387 (num_t) endFeatures.minutesSinceMidnight,
388 (num_t) startFeatures.weekday,
389 weekFeatures[PLACE_CATEG_ID_HOME],
390 weekFeatures[PLACE_CATEG_ID_WORK],
391 weekFeatures[PLACE_CATEG_ID_OTHER]
395 void ctx::VisitCateger::__normalize(std::vector<ctx::num_t> &features)
397 size_t n = features.size();
398 for (size_t i = 0; i < n; i++) {
399 features[i] -= __featuresMean[i];
400 features[i] /= __featuresStd[i];
404 void ctx::VisitCateger::categorize(ctx::Visit &visit)
407 IntervalFeatures features = __intervalFeatures(visit.interval);
408 __normalize(features);
410 for (auto &modelPair : __models) {
411 int categId = modelPair.first;
412 MahalModel model = modelPair.second;
414 num_t distance = model.distance(features);
415 num_t probability = 1 - __chiApprox.value(distance); // sth like probability but not exactly
416 visit.categs[categId] = probability;
projects / platform / core / context / context-provider.git / blob