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// Code generated by genlib2. DO NOT EDIT.
package tensor
import "reflect"
// Ones creates a *Dense with the provided shape and type
func Ones(dt Dtype, shape ...int) *Dense {
d := recycledDense(dt, shape)
switch d.t.Kind() {
case reflect.Int:
d.Memset(int(1))
case reflect.Int8:
d.Memset(int8(1))
case reflect.Int16:
d.Memset(int16(1))
case reflect.Int32:
d.Memset(int32(1))
case reflect.Int64:
d.Memset(int64(1))
case reflect.Uint:
d.Memset(uint(1))
case reflect.Uint8:
d.Memset(uint8(1))
case reflect.Uint16:
d.Memset(uint16(1))
case reflect.Uint32:
d.Memset(uint32(1))
case reflect.Uint64:
d.Memset(uint64(1))
case reflect.Float32:
d.Memset(float32(1))
case reflect.Float64:
d.Memset(float64(1))
case reflect.Complex64:
d.Memset(complex64(1))
case reflect.Complex128:
d.Memset(complex128(1))
case reflect.Bool:
d.Memset(true)
default:
// TODO: add a Oner interface
}
return d
}
// I creates the identity matrix (usually a square) matrix with 1s across the diagonals, and zeroes elsewhere, like so:
// Matrix(4,4)
// ⎡1 0 0 0⎤
// ⎢0 1 0 0⎥
// ⎢0 0 1 0⎥
// ⎣0 0 0 1⎦
// While technically an identity matrix is a square matrix, in attempt to keep feature parity with Numpy,
// the I() function allows you to create non square matrices, as well as an index to start the diagonals.
//
// For example:
// T = I(Float64, 4, 4, 1)
// Yields:
// ⎡0 1 0 0⎤
// ⎢0 0 1 0⎥
// ⎢0 0 0 1⎥
// ⎣0 0 0 0⎦
//
// The index k can also be a negative number:
// T = I(Float64, 4, 4, -1)
// Yields:
// ⎡0 0 0 0⎤
// ⎢1 0 0 0⎥
// ⎢0 1 0 0⎥
// ⎣0 0 1 0⎦
func I(dt Dtype, r, c, k int) *Dense {
ret := New(Of(dt), WithShape(r, c))
i := k
if k < 0 {
i = (-k) * c
}
var s *Dense
var err error
end := c - k
if end > r {
s, err = sliceDense(ret, nil)
} else {
s, err = sliceDense(ret, rs{0, end, 1})
}
if err != nil {
panic(err)
}
var nexts []int
iter := newFlatIterator(&s.AP)
nexts, err = iter.Slice(rs{i, s.Size(), c + 1})
switch s.t.Kind() {
case reflect.Int:
data := s.Ints()
for _, v := range nexts {
data[v] = 1
}
case reflect.Int8:
data := s.Int8s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Int16:
data := s.Int16s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Int32:
data := s.Int32s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Int64:
data := s.Int64s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Uint:
data := s.Uints()
for _, v := range nexts {
data[v] = 1
}
case reflect.Uint8:
data := s.Uint8s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Uint16:
data := s.Uint16s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Uint32:
data := s.Uint32s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Uint64:
data := s.Uint64s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Float32:
data := s.Float32s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Float64:
data := s.Float64s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Complex64:
data := s.Complex64s()
for _, v := range nexts {
data[v] = 1
}
case reflect.Complex128:
data := s.Complex128s()
for _, v := range nexts {
data[v] = 1
}
}
// TODO: create Oner interface for custom types
return ret
}
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