Fix non-breaking typos

autograd/context.v

@@ -13,7 +13,7 @@ pub mut:
 	// This list can contain duplicates
 	nodes []&Node[T]
 	// If no_grad is set to true, operations will not
-	// be cached, and backpropogation will not be possible
+	// be cached, and backpropagation will not be possible
 	no_grad bool
 }
 

autograd/gate.v

@@ -5,7 +5,7 @@ import vtl
 pub interface CacheParam {}
 
 // Gate is an object that can cache the result of an operation,
-// as well as backpropogate a payload backwards along the
+// as well as backpropagate a payload backwards along the
 // computational graph
 //
 // Structs that implement from this interface can add instance

autograd/gates_basic.v

@@ -45,8 +45,8 @@ pub fn subtract_gate[T]() &SubstractGate[T] {
 
 pub fn (g &SubstractGate[T]) backward[T](payload &Payload[T]) ![]&vtl.Tensor[T] {
 	gradient := payload.variable.grad
-	oposite := gradient.multiply_scalar[T](vtl.cast[T](-1))!
-	return [gradient, oposite]
+	opposite := gradient.multiply_scalar[T](vtl.cast[T](-1))!
+	return [gradient, opposite]
 }
 
 pub fn (g &SubstractGate[T]) cache[T](mut result Variable[T], args ...CacheParam) ! {
@@ -134,8 +134,8 @@ pub fn (g &DivideGate[T]) backward[T](payload &Payload[T]) ![]&vtl.Tensor[T] {
 	gradient := payload.variable.grad
 	r0 := gradient.divide[T](g.b.value)!
 	bx2 := g.b.value.multiply_scalar[T](vtl.cast[T](2))!
-	oposite := gradient.multiply_scalar[T](vtl.cast[T](-1))!
-	mut r1 := oposite.multiply[T](g.a.value)!
+	opposite := gradient.multiply_scalar[T](vtl.cast[T](-1))!
+	mut r1 := opposite.multiply[T](g.a.value)!
 	r1 = r1.divide[T](bx2)!
 	return [r0, r1]
 }

autograd/variable.v

@@ -5,7 +5,7 @@ import vtl
 // Variable is an abstraction of a vtl.Tensor that tracks
 // the operations done to the vtl.Tensor. It also keeps
 // track of the gradient of the operation if a Variable
-// needs to backpropogate.
+// needs to backpropagate.
 // This is the fundamental object used in automatic
 // differentiation, as well as the neural network aspects
 // of VTL
@@ -64,7 +64,7 @@ pub fn (v &Variable[T]) str() string {
 	return v.value.str()
 }
 
-// backprop Back propogates an operation along a computational graph.
+// backprop Back propagates an operation along a computational graph.
 // This operation will destroy the operational graph, populating
 // the gradients for all variables that are predecessors of
 // the Variable this is called on.

datasets/loader.v

@@ -48,7 +48,7 @@ struct DatasetDownload {
 fn download_dataset(data DatasetDownload) !string {
 	dataset_dir := os.real_path(get_cache_dir('datasets', data.dataset))
 
-	// Handline extensions like `*.tar.gz`.
+	// Handle extensions like `*.tar.gz`.
 	exts := os.file_name(data.file).rsplit_nth('.', 3)
 	is_tar := exts[0] == 'tar' || (exts.len > 1 && exts[1] == 'tar')
 

examples/vtl_opencl_vcl_support/README.md

@@ -4,7 +4,7 @@ This example shows how to use the OpenCL backend with VCL support.
 
 ## Prerequisites
 
-Read the [docs](https://vlang.github.io/vsl/vcl.html) of the V Computating Language (VCL) and the OpenCL backend.
+Read the [docs](https://vlang.github.io/vsl/vcl.html) of the V Computing Language (VCL) and the OpenCL backend.
 
 ## Running the example
 

src/fun.v

@@ -167,15 +167,13 @@ pub fn (t &Tensor[T]) transpose[T](order []int) !&Tensor[T] {
 	return ret
 }
 
-// t returns a ful transpose of an tensor, with the axes
-// reversed
+// t returns a full transpose of a tensor, with the axes reversed
 pub fn (t &Tensor[T]) t[T]() !&Tensor[T] {
 	order := irange(0, t.rank())
 	return t.transpose(order.reverse())
 }
 
-// swapaxes returns a view of an tensor with two axes
-// swapped.
+// swapaxes returns a view of an tensor with two axes swapped
 pub fn (t &Tensor[T]) swapaxes[T](a1 int, a2 int) !&Tensor[T] {
 	mut order := irange(0, t.rank())
 	tmp := order[a1]

stats/stats.v

@@ -79,7 +79,7 @@ pub fn prod_axis_with_dims[T](t &vtl.Tensor[T], data AxisData) T {
 	return acc
 }
 
-// Measure of Occurance
+// Measure of Occurrence
 // Frequency of a given number
 // Based on
 // https://www.mathsisfun.com/data/frequency-distribution.html
@@ -99,7 +99,7 @@ pub fn freq[T](t &vtl.Tensor[T], val T) int {
 	return count
 }
 
-// Measure of Central Tendancy
+// Measure of Central Tendency
 // Mean of the given input array
 // Based on
 // https://www.mathsisfun.com/data/central-measures.html
@@ -113,7 +113,7 @@ pub fn mean[T](t &vtl.Tensor[T]) T {
 	}) / vtl.cast[T](t.size)
 }
 
-// Measure of Central Tendancy
+// Measure of Central Tendency
 // Geometric Mean of the given input array
 // Based on
 // https://www.mathsisfun.com/numbers/geometric-mean.html
@@ -128,7 +128,7 @@ pub fn geometric_mean[T](t &vtl.Tensor[T]) T {
 	return math.pow(prod, vtl.cast[T](1) / vtl.cast[T](t.size))
 }
 
-// Measure of Central Tendancy
+// Measure of Central Tendency
 // Harmonic Mean of the given input array
 // Based on
 // https://www.mathsisfun.com/numbers/harmonic-mean.html
@@ -142,7 +142,7 @@ pub fn harmonic_mean[T](t &vtl.Tensor[T]) T {
 	})
 }
 
-// Measure of Central Tendancy
+// Measure of Central Tendency
 // Median of the given input array ( input array is assumed to be sorted )
 // Based on
 // https://www.mathsisfun.com/data/central-measures.html
@@ -158,7 +158,7 @@ pub fn median[T](t &vtl.Tensor[T]) T {
 	}
 }
 
-// Measure of Central Tendancy
+// Measure of Central Tendency
 // Mode of the given input array
 // Based on
 // https://www.mathsisfun.com/data/central-measures.html

storage/cpu.v

@@ -25,13 +25,13 @@ pub fn from_array[T](arr []T) &CpuStorage[T] {
 	}
 }
 
-// Private function. Used to implement Storage operator
+// Private function. Used to implement the Storage operator
 @[inline]
 pub fn (s &CpuStorage[T]) get[T](i int) T {
 	return s.data[i]
 }
 
-// Private function. Used to implement assigment to the Storage element
+// Private function. Used to implement assignments to the Storage element
 @[inline]
 pub fn (mut s CpuStorage[T]) set[T](i int, val T) {
 	s.data[i] = val