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
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
|
// Copyright ©2020 The Gonum Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
package interp
import (
"math"
"testing"
"gonum.org/v1/gonum/floats"
)
func TestConstant(t *testing.T) {
t.Parallel()
const value = 42.0
c := Constant(value)
xs := []float64{math.Inf(-1), -11, 0.4, 1e9, math.Inf(1)}
for _, x := range xs {
y := c.Predict(x)
if y != value {
t.Errorf("unexpected Predict(%g) value: got: %g want: %g", x, y, value)
}
}
}
func TestFunction(t *testing.T) {
fn := func(x float64) float64 { return math.Exp(x) }
predictor := Function(fn)
xs := []float64{-100, -1, 0, 0.5, 15}
for _, x := range xs {
want := fn(x)
got := predictor.Predict(x)
if got != want {
t.Errorf("unexpected Predict(%g) value: got: %g want: %g", x, got, want)
}
}
}
func TestFindSegment(t *testing.T) {
t.Parallel()
xs := []float64{0, 1, 2}
testXs := []float64{-0.6, 0, 0.3, 1, 1.5, 2, 2.8}
expectedIs := []int{-1, 0, 0, 1, 1, 2, 2}
for k, x := range testXs {
i := findSegment(xs, x)
if i != expectedIs[k] {
t.Errorf("unexpected value of findSegment(xs, %g): got %d want: %d", x, i, expectedIs[k])
}
}
}
func TestFindSegmentEdgeCases(t *testing.T) {
t.Parallel()
cases := []struct {
xs []float64
x float64
want int
}{
{xs: nil, x: 0, want: -1},
{xs: []float64{0}, x: -1, want: -1},
{xs: []float64{0}, x: 0, want: 0},
{xs: []float64{0}, x: 1, want: 0},
}
for _, test := range cases {
if got := findSegment(test.xs, test.x); got != test.want {
t.Errorf("unexpected value of findSegment(%v, %f): got %d want: %d",
test.xs, test.x, got, test.want)
}
}
}
func BenchmarkFindSegment(b *testing.B) {
xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
for i := 0; i < b.N; i++ {
findSegment(xs, 0)
findSegment(xs, 16.5)
findSegment(xs, -1)
findSegment(xs, 8.25)
findSegment(xs, 4.125)
findSegment(xs, 13.6)
findSegment(xs, 23.6)
findSegment(xs, 13.5)
findSegment(xs, 6)
findSegment(xs, 4.5)
}
}
// testPiecewiseInterpolatorCreation tests common functionality in creating piecewise interpolators.
func testPiecewiseInterpolatorCreation(t *testing.T, fp FittablePredictor) {
type errorParams struct {
xs []float64
ys []float64
}
errorParamSets := []errorParams{
{[]float64{0, 1, 2}, []float64{-0.5, 1.5}},
{[]float64{0.3}, []float64{0}},
{[]float64{0.3, 0.3}, []float64{0, 0}},
{[]float64{0.3, -0.3}, []float64{0, 0}},
}
for _, params := range errorParamSets {
if !panics(func() { _ = fp.Fit(params.xs, params.ys) }) {
t.Errorf("expected panic for xs: %v and ys: %v", params.xs, params.ys)
}
}
}
func TestPiecewiseLinearFit(t *testing.T) {
t.Parallel()
testPiecewiseInterpolatorCreation(t, &PiecewiseLinear{})
}
// testInterpolatorPredict tests evaluation of a interpolator.
func testInterpolatorPredict(t *testing.T, p Predictor, xs []float64, expectedYs []float64, tol float64) {
for i, x := range xs {
y := p.Predict(x)
yErr := math.Abs(y - expectedYs[i])
if yErr > tol {
if tol == 0 {
t.Errorf("unexpected Predict(%g) value: got: %g want: %g", x, y, expectedYs[i])
} else {
t.Errorf("unexpected Predict(%g) value: got: %g want: %g with tolerance: %g", x, y, expectedYs[i], tol)
}
}
}
}
func TestPiecewiseLinearPredict(t *testing.T) {
t.Parallel()
xs := []float64{0, 1, 2}
ys := []float64{-0.5, 1.5, 1}
var pl PiecewiseLinear
err := pl.Fit(xs, ys)
if err != nil {
t.Errorf("Fit error: %s", err.Error())
}
testInterpolatorPredict(t, pl, xs, ys, 0)
testInterpolatorPredict(t, pl, []float64{-0.4, 2.6}, []float64{-0.5, 1}, 0)
testInterpolatorPredict(t, pl, []float64{0.1, 0.5, 0.8, 1.2}, []float64{-0.3, 0.5, 1.1, 1.4}, 1e-15)
}
func BenchmarkNewPiecewiseLinear(b *testing.B) {
xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
ys := []float64{0, 1, 2, 2.5, 2, 1.5, 4, 10, -2, 2}
var pl PiecewiseLinear
for i := 0; i < b.N; i++ {
_ = pl.Fit(xs, ys)
}
}
func BenchmarkPiecewiseLinearPredict(b *testing.B) {
xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
ys := []float64{0, 1, 2, 2.5, 2, 1.5, 4, 10, -2, 2}
var pl PiecewiseLinear
_ = pl.Fit(xs, ys)
for i := 0; i < b.N; i++ {
pl.Predict(0)
pl.Predict(16.5)
pl.Predict(-2)
pl.Predict(4)
pl.Predict(7.32)
pl.Predict(9.0001)
pl.Predict(1.4)
pl.Predict(1.6)
pl.Predict(30)
pl.Predict(13.5)
pl.Predict(4.5)
}
}
func TestNewPiecewiseConstant(t *testing.T) {
var pc PiecewiseConstant
testPiecewiseInterpolatorCreation(t, &pc)
}
func benchmarkPiecewiseConstantPredict(b *testing.B) {
xs := []float64{0, 1.5, 3, 4.5, 6, 7.5, 9, 12, 13.5, 16.5}
ys := []float64{0, 1, 2, 2.5, 2, 1.5, 4, 10, -2, 2}
var pc PiecewiseConstant
_ = pc.Fit(xs, ys)
for i := 0; i < b.N; i++ {
pc.Predict(0)
pc.Predict(16.5)
pc.Predict(4)
pc.Predict(7.32)
pc.Predict(9.0001)
pc.Predict(1.4)
pc.Predict(1.6)
pc.Predict(13.5)
pc.Predict(4.5)
}
}
func BenchmarkPiecewiseConstantPredict(b *testing.B) {
benchmarkPiecewiseConstantPredict(b)
}
func TestPiecewiseConstantPredict(t *testing.T) {
t.Parallel()
xs := []float64{0, 1, 2}
ys := []float64{-0.5, 1.5, 1}
var pc PiecewiseConstant
err := pc.Fit(xs, ys)
if err != nil {
t.Errorf("Fit error: %s", err.Error())
}
testInterpolatorPredict(t, pc, xs, ys, 0)
testXs := []float64{-0.9, 0.1, 0.5, 0.8, 1.2, 3.1}
leftYs := []float64{-0.5, 1.5, 1.5, 1.5, 1, 1}
testInterpolatorPredict(t, pc, testXs, leftYs, 0)
}
func TestCalculateSlopesErrors(t *testing.T) {
t.Parallel()
for _, test := range []struct {
xs, ys []float64
}{
{
xs: []float64{0},
ys: []float64{0},
},
{
xs: []float64{0, 1, 2},
ys: []float64{0, 1}},
{
xs: []float64{0, 0, 1},
ys: []float64{0, 0, 0},
},
{
xs: []float64{0, 1, 0},
ys: []float64{0, 0, 0},
},
} {
if !panics(func() { calculateSlopes(test.xs, test.ys) }) {
t.Errorf("expected panic for xs: %v and ys: %v", test.xs, test.ys)
}
}
}
func TestCalculateSlopes(t *testing.T) {
t.Parallel()
for i, test := range []struct {
xs, ys, want []float64
}{
{
xs: []float64{0, 2, 3, 5},
ys: []float64{0, 1, 1, -1},
want: []float64{0.5, 0, -1},
},
{
xs: []float64{10, 20},
ys: []float64{50, 100},
want: []float64{5},
},
} {
got := calculateSlopes(test.xs, test.ys)
if !floats.EqualApprox(got, test.want, 1e-14) {
t.Errorf("Mismatch in calculated slopes in case %d: got %v, want %v", i, got, test.want)
}
}
}
func applyFunc(xs []float64, f func(x float64) float64) []float64 {
ys := make([]float64, len(xs))
for i, x := range xs {
ys[i] = f(x)
}
return ys
}
func panics(fun func()) (b bool) {
defer func() {
err := recover()
if err != nil {
b = true
}
}()
fun()
return
}
func discrDerivPredict(p Predictor, x0, x1, x, h float64) float64 {
if x <= x0+h {
return (p.Predict(x+h) - p.Predict(x)) / h
} else if x >= x1-h {
return (p.Predict(x) - p.Predict(x-h)) / h
} else {
return (p.Predict(x+h) - p.Predict(x-h)) / (2 * h)
}
}
|
