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package statmodel
import (
"testing"
"gonum.org/v1/gonum/floats"
)
func data1() ([]string, [][]Dtype) {
x := [][]Dtype{
{0, 1, 3, 2, 1, 1, 0},
{1, 1, 1, 1, 1, 1, 1},
{4, 1, -1, 3, 5, -5, 3},
}
return []string{"y", "x1", "x2"}, x
}
func data1b() ([]string, [][]Dtype) {
x := [][]Dtype{
{0, 1, 3, 2, 1, 1, 0},
{1, 1, 1, 1, 1, 1, 1},
{8, 2, -2, 6, 10, -10, 6},
}
return []string{"y", "x1", "x2"}, x
}
func data2() ([]string, [][]Dtype) {
x := [][]Dtype{
{0, 0, 1, 0, 1, 0, 0},
{1, 1, 1, 1, 1, 1, 1},
{4, 1, -1, 3, 5, -5, 3},
{1, -1, 1, 1, 2, 5, -1},
}
return []string{"y", "x1", "x2", "x3"}, x
}
// A mock model for testing
type Mock struct {
data [][]Dtype
xpos []int
}
func (m *Mock) Dataset() [][]Dtype {
return m.data
}
func (m *Mock) LogLike(params Parameter, exact bool) float64 {
return 0
}
func (m *Mock) Score(params Parameter, score []float64) {
}
func (m *Mock) Hessian(params Parameter, ht HessType, score []float64) {
}
func (m *Mock) NumParams() int {
return len(m.xpos)
}
func (m *Mock) NumObs() int {
return len(m.data[0])
}
func (m *Mock) Xpos() []int {
return m.xpos
}
func TestResult1(t *testing.T) {
_, da := data1()
model := &Mock{
data: da,
xpos: []int{1, 2},
}
params := []float64{1, 2}
xnames := []string{"x1", "x2"}
vcov := []float64{0, 0, 0, 0}
r := NewBaseResults(model, 0, params, xnames, vcov)
// Test fitted values on the training data.
fv := []float64{9, 3, -1, 7, 11, -9, 7}
if !floats.Equal(fv, r.FittedValues(nil)) {
t.Fail()
}
// Test fitted values when passing a new data stream.
_, da2 := data1b()
fv = []float64{17, 5, -3, 13, 21, -19, 13}
if !floats.Equal(fv, r.FittedValues(da2)) {
t.Fail()
}
}
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