coreylowman · coreylowman · Feb 23, 2023 · Feb 23, 2023
diff --git a/src/data/arange.rs b/src/data/arange.rs
@@ -1,12 +1,12 @@
 use crate::{
     shapes::*,
-    tensor::{CopySlice, DeviceStorage, Tensor, ZerosTensor},
+    tensor::{DeviceStorage, Tensor, TensorFromVec, ZerosTensor},
 };
 
 use std::vec::Vec;
 
 /// Generates a tensor with ordered data from 0 to `N`.
-pub trait Arange<E: Dtype>: DeviceStorage + ZerosTensor<E> + CopySlice<E> {
+pub trait Arange<E: Dtype>: DeviceStorage + ZerosTensor<E> + TensorFromVec<E> {
     /// Generates a tensor with ordered data from 0 to `N`.
     ///
     /// Const sized tensor:
@@ -29,9 +29,7 @@ pub trait Arange<E: Dtype>: DeviceStorage + ZerosTensor<E> + CopySlice<E> {
         for i in 0..n.size() {
             data.push(E::from_usize(i).unwrap());
         }
-        let mut t = self.zeros_like(&(n,));
-        t.copy_from(&data);
-        t
+        self.tensor_from_vec(data, (n,))
     }
 }
-impl<E: Dtype, D: ZerosTensor<E> + CopySlice<E>> Arange<E> for D {}
+impl<E: Dtype, D: ZerosTensor<E> + TensorFromVec<E>> Arange<E> for D {}
diff --git a/src/data/one_hot_encode.rs b/src/data/one_hot_encode.rs
@@ -2,12 +2,12 @@ use std::vec::Vec;
 
 use crate::{
     shapes::*,
-    tensor::{CopySlice, DeviceStorage, Tensor, ZerosTensor},
+    tensor::{DeviceStorage, Tensor, TensorFromVec, ZerosTensor},
 };
 
 /// One hot encodes an array of class labels into a 2d tensor of probability
 /// vectors. This can be used in tandem with [crate::losses::cross_entropy_with_logits_loss()].
-pub trait OneHotEncode<E: Dtype>: DeviceStorage + ZerosTensor<E> + CopySlice<E> {
+pub trait OneHotEncode<E: Dtype>: DeviceStorage + ZerosTensor<E> + TensorFromVec<E> {
     /// One hot encodes an array or vec into a tensor.
     ///
     /// Arguments:
@@ -89,9 +89,7 @@ pub trait OneHotEncode<E: Dtype>: DeviceStorage + ZerosTensor<E> + CopySlice<E>
                 });
             }
         }
-        let mut t = self.zeros_like(&(l, n));
-        t.copy_from(&data);
-        t
+        self.tensor_from_vec(data, (l, n))
     }
 }
-impl<E: Dtype, D: DeviceStorage + ZerosTensor<E> + CopySlice<E>> OneHotEncode<E> for D {}
+impl<E: Dtype, D: DeviceStorage + ZerosTensor<E> + TensorFromVec<E>> OneHotEncode<E> for D {}
diff --git a/src/tensor/mod.rs b/src/tensor/mod.rs
@@ -125,7 +125,7 @@ pub use cpu::{Cpu, CpuError};
 #[cfg(feature = "cuda")]
 pub use cuda::{Cuda, CudaError};
 
-pub use storage_traits::{AsArray, AsVec, CopySlice, TensorFrom};
+pub use storage_traits::{AsArray, AsVec, CopySlice, TensorFrom, TensorFromVec};
 pub use storage_traits::{DeviceStorage, HasErr};
 pub use storage_traits::{OnesTensor, SampleTensor, ZerosTensor};
 

diff --git a/src/tensor_ops/utilities/device.rs b/src/tensor_ops/utilities/device.rs
@@ -8,7 +8,7 @@ use crate::{
 pub trait Device<E: Dtype>:
     DeviceStorage
     + CopySlice<E>
-    + crate::tensor::storage_traits::TensorFromVec<E>
+    + crate::tensor::TensorFromVec<E>
 
     // allocation
     + crate::tensor::ZerosTensor<E>