e2a3b66123
Adds unit tests covering tensor operations, type enums, client functionality, and serialization for all 12 SDK implementations: - JavaScript (Vitest): tensor, types, client tests - Python (pytest): tensor, types, client tests - Go: standard library tests with httptest - Flutter (flutter_test): tensor, types tests - Java (JUnit 5): tensor, types tests - Rust: embedded module tests - Ruby (minitest): tensor, types tests - C# (xUnit): tensor, types tests Tests cover: - Tensor creation (zeros, ones, random, randn, eye, arange, linspace) - Tensor operations (reshape, transpose, indexing) - Reductions (sum, mean, std, min, max) - Activations (relu, sigmoid, softmax) - Serialization/deserialization - Type enums and configuration - Client request building - Error handling
117 lines
3.1 KiB
Ruby
117 lines
3.1 KiB
Ruby
# frozen_string_literal: true
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require 'minitest/autorun'
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require_relative '../lib/synor_compute'
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class TestTensor < Minitest::Test
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def test_creation
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tensor = SynorCompute::Tensor.new([2, 3], [1.0, 2.0, 3.0, 4.0, 5.0, 6.0])
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assert_equal [2, 3], tensor.shape
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assert_equal 6, tensor.size
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assert_equal 2, tensor.ndim
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end
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def test_zeros
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tensor = SynorCompute::Tensor.zeros([3, 4])
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assert_equal [3, 4], tensor.shape
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assert_equal 12, tensor.size
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assert tensor.data.all? { |v| v == 0.0 }
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end
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def test_ones
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tensor = SynorCompute::Tensor.ones([2, 2])
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assert tensor.data.all? { |v| v == 1.0 }
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end
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def test_random
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tensor = SynorCompute::Tensor.rand([10, 10])
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assert_equal [10, 10], tensor.shape
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assert tensor.data.all? { |v| v >= 0 && v < 1 }
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end
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def test_randn
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tensor = SynorCompute::Tensor.randn([1000])
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mean = tensor.mean
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std = tensor.std
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assert mean.abs < 0.2, "Mean should be close to 0, got #{mean}"
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assert std > 0.8 && std < 1.2, "Std should be close to 1, got #{std}"
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end
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def test_eye
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tensor = SynorCompute::Tensor.eye(3)
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assert_equal [3, 3], tensor.shape
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assert_equal 1.0, tensor[[0, 0]]
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assert_equal 1.0, tensor[[1, 1]]
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assert_equal 0.0, tensor[[0, 1]]
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end
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def test_arange
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tensor = SynorCompute::Tensor.arange(0, 5, 1)
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assert_equal [5], tensor.shape
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assert_equal 0.0, tensor[[0]]
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assert_equal 4.0, tensor[[4]]
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end
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def test_linspace
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tensor = SynorCompute::Tensor.linspace(0, 10, 11)
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assert_equal [11], tensor.shape
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assert_in_delta 0.0, tensor[[0]], 0.0001
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assert_in_delta 10.0, tensor[[10]], 0.0001
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end
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def test_reshape
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tensor = SynorCompute::Tensor.new([6], [1, 2, 3, 4, 5, 6])
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reshaped = tensor.reshape([2, 3])
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assert_equal [2, 3], reshaped.shape
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assert_equal 6, reshaped.size
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end
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def test_transpose
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tensor = SynorCompute::Tensor.new([2, 3], [1, 2, 3, 4, 5, 6])
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transposed = tensor.transpose
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assert_equal [3, 2], transposed.shape
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end
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def test_sum
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tensor = SynorCompute::Tensor.new([4], [1, 2, 3, 4])
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assert_equal 10.0, tensor.sum
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end
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def test_mean
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tensor = SynorCompute::Tensor.new([4], [1, 2, 3, 4])
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assert_equal 2.5, tensor.mean
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end
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def test_min_max
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tensor = SynorCompute::Tensor.new([4], [3, 1, 4, 2])
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assert_equal 1.0, tensor.min
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assert_equal 4.0, tensor.max
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end
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def test_relu
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tensor = SynorCompute::Tensor.new([5], [-2, -1, 0, 1, 2])
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result = tensor.relu
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assert_equal [0, 0, 0, 1, 2], result.data
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end
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def test_sigmoid
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tensor = SynorCompute::Tensor.new([1], [0])
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result = tensor.sigmoid
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assert_in_delta 0.5, result[[0]], 0.0001
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end
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def test_softmax
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tensor = SynorCompute::Tensor.new([3], [1, 2, 3])
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result = tensor.softmax
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assert_in_delta 1.0, result.sum, 0.0001
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assert result[[2]] > result[[1]]
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end
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def test_serialization
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original = SynorCompute::Tensor.new([2, 3], [1, 2, 3, 4, 5, 6])
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json = original.to_json
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restored = SynorCompute::Tensor.from_json(json)
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assert_equal original.shape, restored.shape
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assert_equal original.data, restored.data
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end
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end
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