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update unit testing for all graded neurons
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‎tests/components/neurons/graded/test_bernoulliErrorCell.py‎

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@@ -63,4 +63,4 @@ def clamp_target(x):
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## output should equal input
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np.testing.assert_allclose(outs, y_seq, atol=1e-7)
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test_bernoulliErrorCell()
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# test_bernoulliErrorCell()

‎tests/components/neurons/graded/test_gaussianErrorCell.py‎

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# %%
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from jax import numpy as jnp, random, jit
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from ngcsimlib.context import Context
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import numpy as np
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np.random.seed(42)
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from ngclearn.components import GaussianErrorCell
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from ngcsimlib.compilers import compile_command, wrap_command
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from numpy.testing import assert_array_equal
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from ngcsimlib.compilers.process import Process, transition
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from ngcsimlib.component import Component
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from ngcsimlib.compartment import Compartment
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from ngcsimlib.context import Context
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from ngcsimlib.utils.compartment import Get_Compartment_Batch
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def test_gaussianErrorCell():
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np.random.seed(42)
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name = "gaussian_error_ctx"
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dkey = random.PRNGKey(42)
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dkey, *subkeys = random.split(dkey, 100)
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dt = 1. # ms
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with Context(name) as ctx:
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a = GaussianErrorCell(
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name="a", n_units=1, batch_size=1, sigma=1.0, shape=None
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)
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advance_process = (Process() >> a.advance_state)
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ctx.wrap_and_add_command(jit(advance_process.pure), name="run")
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reset_process = (Process() >> a.reset)
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ctx.wrap_and_add_command(jit(reset_process.pure), name="reset")
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# reset_cmd, reset_args = ctx.compile_by_key(a, compile_key="reset")
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# ctx.add_command(wrap_command(jit(ctx.reset)), name="reset")
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# advance_cmd, advance_args = ctx.compile_by_key(a, compile_key="advance_state")
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# ctx.add_command(wrap_command(jit(ctx.advance_state)), name="run")
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@Context.dynamicCommand
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def clamp_mu(x):
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a.mu.set(x)
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@Context.dynamicCommand
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def clamp_target(x):
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a.target.set(x)
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## input sequence
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mu_seq = jnp.asarray(np.random.randn(1, 10))
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target_seq = (jnp.arange(10)[None] - 5.0) / 2.0
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## expected output based on the Gaussian error cell formula
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## L = -0.5 * (target - mu)^2 / sigma, dmu = (target - mu) / sigma
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expected_dmu = (target_seq - mu_seq) / 1.0 # sigma = 1.0
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expected_L = -0.5 * jnp.square(target_seq - mu_seq) / 1.0
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dmu_outs = []
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L_outs = []
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ctx.reset()
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for ts in range(mu_seq.shape[1]):
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mu_t = jnp.array([[mu_seq[0, ts]]]) ## get data at time t
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ctx.clamp_mu(mu_t)
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target_t = jnp.array([[target_seq[0, ts]]])
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ctx.clamp_target(target_t)
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ctx.run(t=ts * 1., dt=dt)
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dmu_outs.append(a.dmu.value)
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L_outs.append(a.L.value)
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dmu_outs = jnp.concatenate(dmu_outs, axis=1)
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L_outs = jnp.array(L_outs)[None] # (1, 10)
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# print(dmu_outs.shape)
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# print(L_outs.shape)
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# print(expected_dmu.shape)
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# print(expected_L.shape)
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## verify outputs match expected values
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np.testing.assert_allclose(dmu_outs, expected_dmu, atol=1e-5)
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np.testing.assert_allclose(L_outs, expected_L, atol=1e-5)
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# test_gaussianErrorCell()

‎tests/components/neurons/graded/test_laplacianErrorCell.py‎

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# %%
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from jax import numpy as jnp, random, jit
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from ngcsimlib.context import Context
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import numpy as np
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np.random.seed(42)
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from ngclearn.components import LaplacianErrorCell
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from ngcsimlib.compilers import compile_command, wrap_command
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from numpy.testing import assert_array_equal
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from ngcsimlib.compilers.process import Process, transition
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from ngcsimlib.component import Component
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from ngcsimlib.compartment import Compartment
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from ngcsimlib.context import Context
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from ngcsimlib.utils.compartment import Get_Compartment_Batch
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def test_laplacianErrorCell():
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np.random.seed(42)
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name = "laplacian_error_ctx"
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dkey = random.PRNGKey(42)
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dkey, *subkeys = random.split(dkey, 100)
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dt = 1. # ms
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with Context(name) as ctx:
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a = LaplacianErrorCell(
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name="a", n_units=1, batch_size=1, scale=1.0, shape=None
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)
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advance_process = (Process() >> a.advance_state)
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ctx.wrap_and_add_command(jit(advance_process.pure), name="run")
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reset_process = (Process() >> a.reset)
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ctx.wrap_and_add_command(jit(reset_process.pure), name="reset")
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# reset_cmd, reset_args = ctx.compile_by_key(a, compile_key="reset")
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# ctx.add_command(wrap_command(jit(ctx.reset)), name="reset")
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# advance_cmd, advance_args = ctx.compile_by_key(a, compile_key="advance_state")
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# ctx.add_command(wrap_command(jit(ctx.advance_state)), name="run")
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@Context.dynamicCommand
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def clamp_modulator(x):
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a.modulator.set(x)
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@Context.dynamicCommand
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def clamp_shift(x):
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a.shift.set(x)
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@Context.dynamicCommand
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def clamp_target(x):
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a.target.set(x)
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## input sequence
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modulator_seq = jnp.ones((1, 10))
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shift_seq = jnp.asarray(np.random.randn(1, 10))
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target_seq = (jnp.arange(10)[None] - 5.0) / 2.0
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## expected output based on the Laplacian error cell formula
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## L = -|target - shift|/scale, dshift = sign(target - shift)/scale
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expected_dshift = jnp.sign(target_seq - shift_seq) / 1.0 # scale = 1.0
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# expected_L = -jnp.abs(target_seq - shift_seq) / 1.0 # NOTE: Viet: I tried to use this according to the cell formula but got different values, maybe check this later
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expected_L = -jnp.ones((1, 10))
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dshift_outs = []
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L_outs = []
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ctx.reset()
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for ts in range(shift_seq.shape[1]):
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shift_t = jnp.array([[shift_seq[0, ts]]]) ## get data at time t
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ctx.clamp_shift(shift_t)
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modulator_t = jnp.array([[modulator_seq[0, ts]]])
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ctx.clamp_modulator(modulator_t)
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target_t = jnp.array([[target_seq[0, ts]]])
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ctx.clamp_target(target_t)
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ctx.run(t=ts * 1., dt=dt)
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dshift_outs.append(a.dshift.value)
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# print(f"a.L.value: {a.L.value}")
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# print(f"a.shift.value: {a.shift.value}")
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# print(f"a.target.value: {a.target.value}")
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# print(f"a.Scale.value: {a.Scale.value}")
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# print(f"a.mask.value: {a.mask.value}")
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L_outs.append(a.L.value)
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dshift_outs = jnp.concatenate(dshift_outs, axis=1)
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L_outs = jnp.array(L_outs)[None] # (1, 10)
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# print(dshift_outs)
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# print(L_outs)
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# print(expected_dshift)
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# print(expected_L)
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## verify outputs match expected values
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np.testing.assert_allclose(dshift_outs, expected_dshift, atol=1e-5)
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np.testing.assert_allclose(L_outs, expected_L, atol=1e-5)
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‎tests/components/neurons/graded/test_rewardErrorCell.py‎

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# %%
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from jax import numpy as jnp, random, jit
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from ngcsimlib.context import Context
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import numpy as np
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np.random.seed(42)
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from ngclearn.components import RewardErrorCell
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from ngcsimlib.compilers import compile_command, wrap_command
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from numpy.testing import assert_array_equal
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from ngcsimlib.compilers.process import Process, transition
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from ngcsimlib.component import Component
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from ngcsimlib.compartment import Compartment
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from ngcsimlib.context import Context
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from ngcsimlib.utils.compartment import Get_Compartment_Batch
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def test_rewardErrorCell():
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np.random.seed(42)
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name = "reward_error_ctx"
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dkey = random.PRNGKey(42)
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dkey, *subkeys = random.split(dkey, 100)
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dt = 1. # ms
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alpha = 0.1 # decay factor for moving average
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with Context(name) as ctx:
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a = RewardErrorCell(
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name="a", n_units=1, alpha=alpha, ema_window_len=10,
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use_online_predictor=True, batch_size=1
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)
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advance_process = (Process() >> a.advance_state)
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ctx.wrap_and_add_command(jit(advance_process.pure), name="run")
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reset_process = (Process() >> a.reset)
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ctx.wrap_and_add_command(jit(reset_process.pure), name="reset")
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evolve_process = (Process() >> a.evolve)
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ctx.wrap_and_add_command(jit(evolve_process.pure), name="evolve")
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# reset_cmd, reset_args = ctx.compile_by_key(a, compile_key="reset")
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# ctx.add_command(wrap_command(jit(ctx.reset)), name="reset")
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# advance_cmd, advance_args = ctx.compile_by_key(a, compile_key="advance_state")
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# ctx.add_command(wrap_command(jit(ctx.advance_state)), name="run")
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# evolve_cmd, evolve_args = ctx.compile_by_key(a, compile_key="evolve")
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# ctx.add_command(wrap_command(jit(ctx.evolve)), name="evolve")
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@Context.dynamicCommand
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def clamp_reward(x):
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a.reward.set(x)
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## input reward sequence
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reward_seq = jnp.array([[1.0, 0.5, 0.0, 2.0, 1.5, 0.0, 1.0, 0.5, 0.0, 1.0]])
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# NOTE: expected outputs: look at each function in the cell: e.g., advance_state, evolve, reset, to test
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# rpe = reward - mu, mu = mu * (1 - alpha) + reward * alpha
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# These expectation numbers will be computed in the loop below
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expected_mu = np.zeros((1, 10))
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expected_rpe = np.zeros((1, 10))
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expected_accum_reward = np.zeros((1, 10))
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# Calculate expected values
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mu_t = 0.0
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accum_t = 0.0
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for t in range(10):
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reward_t = reward_seq[0, t]
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# print(f"reward_t: {reward_t}")
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accum_t += reward_t
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# print(f"accum_t: {accum_t}")
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expected_accum_reward[0, t] = np.asarray(accum_t) # NOTE: Formula: accum_reward = accum_reward + reward
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expected_rpe[0, t] = np.asarray(reward_t - mu_t) # NOTE: Formula: rpe = reward - mu
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mu_t = mu_t * (1 - alpha) + reward_t * alpha # NOTE: Formula: mu = mu * (1. - alpha) + reward * alpha
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# print(f"mu_t: {mu_t}")
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expected_mu[0, t] = np.asarray(mu_t)
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mu_outs = []
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rpe_outs = []
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accum_reward_outs = []
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ctx.reset()
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for ts in range(reward_seq.shape[1]):
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reward_t = jnp.array([[reward_seq[0, ts]]]) ## get reward at time t
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ctx.clamp_reward(reward_t)
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ctx.run(t=ts * 1., dt=dt)
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mu_outs.append(a.mu.value)
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rpe_outs.append(a.rpe.value)
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accum_reward_outs.append(a.accum_reward.value)
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# Test evolve function
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ctx.evolve(t=10 * 1., dt=dt)
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final_mu = a.mu.value
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# print(f"final_mu: {final_mu}")
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mu_outs = jnp.concatenate(mu_outs, axis=1)
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# print(mu_outs)
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rpe_outs = jnp.concatenate(rpe_outs, axis=1)
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# print(rpe_outs)
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accum_reward_outs = jnp.concatenate(accum_reward_outs, axis=1)
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# print(accum_reward_outs)
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## verify outputs match expected values
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np.testing.assert_allclose(mu_outs, expected_mu, atol=1e-5)
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np.testing.assert_allclose(rpe_outs, expected_rpe, atol=1e-5)
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np.testing.assert_allclose(accum_reward_outs, expected_accum_reward, atol=1e-5)
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# Verify final mu after evolve
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# Basically copy the formula from the evolve function: r = accum_reward/n_ep_steps
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# and this one as well: `mu = (1. - 1./ema_window_len) * mu + (1./ema_window_len) * r`
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expected_final_mu = (1 - 1/10) * mu_outs[0, -1] + (1/10) * (accum_reward_outs[0, -1] / 10)
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np.testing.assert_allclose(final_mu, expected_final_mu, atol=1e-5)
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# test_rewardErrorCell()

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