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package tensor
import (
"slices"
"testing"
)
func TestNewDense_Strides(t *testing.T) {
d := NewDense[float32]([]int{2, 3, 4})
expected := []int{12, 4, 1}
if !slices.Equal(d.strides, expected) {
t.Fatalf("expected %v but got %v", expected, d.strides)
}
}
func TestDense_IsContiguous(t *testing.T) {
d := NewDense[float32]([]int{2, 3})
if !d.IsContiguous() {
t.Fatalf("expected contiguous true")
}
permuted := d.Permute([]int{1, 0})
if permuted.IsContiguous() {
t.Fatalf("expected contiguous false")
}
}
func TestDense_IsContiguousStrict(t *testing.T) {
d := NewDense[int]([]int{1, 3})
v := Dense[int]{
base: d.base,
offset: 0,
shape: []int{1, 3},
strides: []int{999, 1},
}
if v.IsContiguous() {
t.Fatalf("expected contiguous false")
}
}
func TestDense_Contiguous(t *testing.T) {
d := NewDense[float32]([]int{2, 3})
permuted := d.Permute([]int{1, 0})
contiguous := permuted.Contiguous()
if !contiguous.IsContiguous() {
t.Fatalf("expected contiguous true")
}
}
func TestDense_Permute(t *testing.T) {
d := NewDense[float32]([]int{2, 3})
for i := range d.Size() {
d.base[i] = float32(i)
}
permuted := d.Permute([]int{1, 0})
expected := []float32{
0, 3,
1, 4,
2, 5,
}
buffer := make([]int, permuted.Rank())
linear := 0
for _, v := range permuted.All(buffer) {
a := v
b := expected[linear]
if a != b {
t.Fatalf("expected %.2f but got %.2f at linear %d", b, a, linear)
}
linear++
}
}
func TestDense_All(t *testing.T) {
d := NewDense[float32]([]int{2, 3})
for i := range d.Size() {
d.base[i] = float32(i)
}
d = d.Permute([]int{1, 0})
out := []float32{
0, 3,
1, 4,
2, 5,
}
buffer := make([]int, d.Rank())
step := 0
for idxs := range d.All(buffer) {
a := d.At(idxs)
b := out[step]
if a != b {
t.Fatalf("expected %.2f but got %.2f", a, b)
}
step++
}
if step != d.Size() {
t.Fatalf("premature termination")
}
}
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