95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
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"""Tests for Liquid Neural Networks module."""
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from __future__ import annotations
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from fusionagi.reasoning.liquid_networks import LiquidCell, LiquidNetwork, LiquidNetworkConfig
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class TestLiquidCell:
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def test_init_defaults(self) -> None:
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cell = LiquidCell(input_dim=4, hidden_dim=3)
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assert len(cell.w_in) == 3
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assert len(cell.w_in[0]) == 4
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assert len(cell.state) == 3
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def test_step_changes_state(self) -> None:
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cell = LiquidCell(input_dim=2, hidden_dim=2)
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initial = list(cell.state)
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cell.step([1.0, 0.5])
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assert cell.state != initial
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def test_reset_zeros_state(self) -> None:
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cell = LiquidCell(input_dim=2, hidden_dim=2)
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cell.step([1.0, 0.5])
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cell.reset()
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assert all(s == 0.0 for s in cell.state)
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def test_multiple_steps_evolve(self) -> None:
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cell = LiquidCell(input_dim=3, hidden_dim=4)
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states = []
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for _ in range(5):
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states.append(list(cell.step([0.5, -0.3, 0.8])))
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assert states[0] != states[4]
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class TestLiquidNetwork:
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def test_init_default_config(self) -> None:
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net = LiquidNetwork()
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assert net.config.input_dim == 64
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def test_forward_output_shape(self) -> None:
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cfg = LiquidNetworkConfig(input_dim=8, hidden_dim=4, output_dim=3, num_layers=1)
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net = LiquidNetwork(cfg)
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out = net.forward([1.0] * 8)
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assert len(out) == 3
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def test_forward_padding(self) -> None:
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cfg = LiquidNetworkConfig(input_dim=8, hidden_dim=4, output_dim=2)
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net = LiquidNetwork(cfg)
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out = net.forward([1.0, 2.0]) # Shorter than input_dim
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assert len(out) == 2
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def test_forward_truncation(self) -> None:
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cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=2, output_dim=2)
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net = LiquidNetwork(cfg)
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out = net.forward([1.0] * 10) # Longer than input_dim
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assert len(out) == 2
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def test_forward_sequence(self) -> None:
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cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=3, output_dim=2, num_layers=1)
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net = LiquidNetwork(cfg)
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inputs = [[float(i)] * 4 for i in range(5)]
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outputs = net.forward_sequence(inputs)
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assert len(outputs) == 5
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assert all(len(o) == 2 for o in outputs)
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def test_reset_clears_state(self) -> None:
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cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=3, output_dim=2)
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net = LiquidNetwork(cfg)
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net.forward([1.0] * 4)
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net.reset()
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for layer in net._layers:
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assert all(s == 0.0 for s in layer.state)
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def test_adapt_weights(self) -> None:
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cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=3, output_dim=2, num_layers=1)
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net = LiquidNetwork(cfg)
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inputs = [[0.1, 0.2, 0.3, 0.4], [0.5, 0.6, 0.7, 0.8]]
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targets = [[0.5, -0.5], [0.3, 0.3]]
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result = net.adapt_weights(inputs, targets, epochs=5)
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assert "final_loss" in result
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assert result["epochs_run"] <= 5
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def test_get_summary(self) -> None:
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net = LiquidNetwork()
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summary = net.get_summary()
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assert summary["type"] == "LiquidNetwork"
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assert "total_parameters" in summary
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def test_output_bounded(self) -> None:
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cfg = LiquidNetworkConfig(input_dim=4, hidden_dim=4, output_dim=3)
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net = LiquidNetwork(cfg)
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out = net.forward([10.0, -10.0, 5.0, -5.0])
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for val in out:
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assert -1.0 <= val <= 1.0
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