1.0.0 breaking changes (#65)

* 1.0.0 breaking changes

* ignore versions for now

Author: christopherzimmerman

.github/workflows/crystal.yml

@@ -13,6 +13,6 @@ jobs:
     steps:
     - uses: actions/checkout@v1
     - name: Install dependencies
-      run: shards install
+      run: shards install --ignore-crystal-version
     - name: Run tests
       run: crystal spec

shard.yml

@@ -1,10 +1,10 @@
 name: num
-version: 0.4.4
+version: 0.4.5
 
 authors:
   - Chris Zimmerman <[email protected]>
 
-crystal: 0.35.1
+crystal: 1.0.0
 
 license: MIT
 
@@ -13,6 +13,7 @@ dependencies:
     github: crystal-data/opencl.cr
   alea:
     github: nin93/alea
+    branch: master
 
 scripts:
   postinstall: make ext

src/grad/gates_blas.cr

@@ -41,7 +41,7 @@ class Num::Grad::MatMulGate(T) < Num::Grad::Gate(T)
   end
 
   # :nodoc:
-  def cache(result : Num::Grad::Variable(T), *args : Num::Grad::Variable(T))
+  def cache(result : Num::Grad::Variable(T), *args)
     a, b = args
 
     result.grad = T.zeros_like(result.value)

src/nn/gates/elu.cr

@@ -32,7 +32,7 @@ class Num::NN::EluGate(T) < Num::Grad::Gate(T)
     [Num::NN.elu_prime(gradient, @cache)]
   end
 
-  def cache(result : Num::Grad::Variable(T), *args : Num::Grad::Variable(T))
+  def cache(result : Num::Grad::Variable(T), *args)
     result.grad = T.zeros_like(result.value)
     result.requires_grad = true
 

src/nn/gates/input.cr

@@ -30,7 +30,7 @@ class Num::NN::InputGate(T) < Num::Grad::Gate(T)
     [gradient]
   end
 
-  def cache(result : Num::Grad::Variable(T), *args : Num::Grad::Variable(T))
+  def cache(result : Num::Grad::Variable(T), *args)
     result.grad = T.zeros_like(result.value)
     result.requires_grad = true
 

src/nn/gates/leaky_relu.cr

@@ -32,7 +32,7 @@ class Num::NN::LeakyReluGate(T) < Num::Grad::Gate(T)
     [Num::NN.leaky_relu_prime(gradient, @cache)]
   end
 
-  def cache(result : Num::Grad::Variable(T), *args : Num::Grad::Variable(T))
+  def cache(result : Num::Grad::Variable(T), *args)
     result.grad = T.zeros_like(result.value)
     result.requires_grad = true
 

src/nn/gates/linear.cr

@@ -53,7 +53,7 @@ class Num::NN::LinearGate(T) < Num::Grad::Gate(T)
     result
   end
 
-  def cache(result : Num::Grad::Variable(T), *args : Num::Grad::Variable(T))
+  def cache(result : Num::Grad::Variable(T), *args)
     input, weight, bias = args
     result.grad = T.zeros_like(result.value)
     Num::Grad.register("Linear", self, result, input, weight, bias)

src/nn/gates/relu.cr

@@ -32,7 +32,7 @@ class Num::NN::ReluGate(T) < Num::Grad::Gate(T)
     [Num::NN.relu_prime(gradient, @cache)]
   end
 
-  def cache(result : Num::Grad::Variable(T), *args : Num::Grad::Variable(T))
+  def cache(result : Num::Grad::Variable(T), *args)
     result.grad = T.zeros_like(result.value)
     result.requires_grad = true
 

src/nn/gates/sigmoid.cr

@@ -32,7 +32,7 @@ class Num::NN::SigmoidGate(T) < Num::Grad::Gate(T)
     [Num::NN.sigmoid_prime(gradient, @cache)]
   end
 
-  def cache(result : Num::Grad::Variable(T), *args : Num::Grad::Variable(T))
+  def cache(result : Num::Grad::Variable(T), *args)
     result.grad = T.zeros_like(result.value)
     result.requires_grad = true
 

src/nn/initialization.cr

@@ -44,7 +44,7 @@ module Num::NN
     end
 
     product = 1
-    2.step(to: shape.size - 1) do |i|
+    1.step(to: shape.size - 1) do |i|
       product *= shape[i]
     end
 

src/tensor/linalg.cr

@@ -171,7 +171,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # t = [[0, 1], [1, 1], [1, 1], [2, 1]].to_tensor.as_type(Float32)
   # q, r = t.qr
   # puts q
@@ -210,7 +210,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # t = [[0, 1], [1, 1], [1, 1], [2, 1]].to_tensor.as_type(Float32)
   # a, b, c = t.svd
   # puts a
@@ -246,7 +246,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # t = [[0, 1], [1, 1]].to_tensor.as_type(Float32)
   # w, v = t.eigh
   # puts w
@@ -281,7 +281,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # t = [[0, 1], [1, 1]].to_tensor.as_type(Float32)
   # w, v = t.eig
   # puts w
@@ -369,7 +369,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # t = [[0, 1], [1, 1], [1, 1], [2, 1]].to_tensor.as_type(Float32)
   # t.norm # => 3.6055512
   # ```
@@ -388,7 +388,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # t = [[1, 2], [3, 4]].to_tensor.astype(Float32)
   # puts t.det # => -2.0
   # ```
@@ -419,7 +419,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # t = [[1, 2], [3, 4]].to_tensor.as_type(Float32)
   # puts t.inv
   #
@@ -448,7 +448,7 @@ class Tensor(T)
   #
   # Examples
   # --------
-  # ```crystal
+  # ```
   # a = [[3, 1], [1, 2]].to_tensor.astype(Float32)
   # b = [9, 8].to_tensor.astype(Float32)
   # puts a.solve(b)

src/tensor/tensor.cr

@@ -1776,7 +1776,7 @@ class Tensor(T)
         arg = arg.begin...@shape[i]
       end
     end
-    s, o = Indexable.range_to_index_and_count(arg, @shape[i])
+    s, o = Indexable.range_to_index_and_count(arg, @shape[i]).not_nil!
     if s >= @shape[i]
       raise Num::Internal::IndexError.new(
         "Index #{arg} out of range for axis #{i} with size #{@shape[i]}"
@@ -1788,7 +1788,7 @@ class Tensor(T)
   private def normalize(arg : Tuple(Range(B, E), Int), i : Int32) forall B, E
     range, step = arg
     abs_step = step.abs
-    start, offset = Indexable.range_to_index_and_count(range, @shape[i])
+    start, offset = Indexable.range_to_index_and_count(range, @shape[i]).not_nil!
     if start >= @shape[i]
       raise Num::Internal::IndexError.new(
         "Index #{arg} out of range for axis #{i} with size #{@shape[i]}"