modifying

examples/a.py

@@ -1,55 +0,0 @@
-import numpy as np
-
-import jax.numpy as jnp
-import jax
-
-a = {1:2, 2:3, 4:5}
-print(a.values())
-
-a = jnp.array([1, 0, 1, 0, np.nan])
-b = jnp.array([1, 1, 1, 1, 1])
-c = jnp.array([1, 1, 1, 1, 1])
-
-full = jnp.array([
-    [1, 1, 1],
-    [0, 1, 1],
-    [1, 1, 1],
-    [0, 1, 1],
-])
-
-print(jnp.column_stack([a[:, None], b[:, None], c[:, None]]))
-
-aux0 = full[:, 0, None]
-aux1 = full[:, 1, None]
-
-print(aux0, aux0.shape)
-
-print(jnp.concatenate([aux0, aux1], axis=1))
-
-f_a = jnp.array([False, False, True, True])
-f_b = jnp.array([True, False, False, False])
-
-print(jnp.logical_and(f_a, f_b))
-print(f_a & f_b)
-
-print(f_a + jnp.nan * 0.0)
-print(f_a + 1 * 0.0)
-
-
-@jax.jit
-def main():
-    return func('happy') + func('sad')
-
-
-def func(x):
-    if x == 'happy':
-        return 1
-    else:
-        return 2
-
-a = jnp.zeros((3, 3))
-print(a.dtype)
-
-c = None
-b = 1 or c
-print(b)

examples/evox_test.py

@@ -0,0 +1,26 @@
+import jax
+from jax import numpy as jnp
+
+from evox import algorithms, problems, pipelines
+from evox.monitors import StdSOMonitor
+
+monitor = StdSOMonitor()
+
+pso = algorithms.PSO(
+    lb=jnp.full(shape=(2,), fill_value=-32),
+    ub=jnp.full(shape=(2,), fill_value=32),
+    pop_size=100,
+)
+
+ackley = problems.classic.Ackley()
+
+pipeline = pipelines.StdPipeline(pso, ackley, fitness_transform=monitor.record_fit)
+
+key = jax.random.PRNGKey(42)
+state = pipeline.init(key)
+
+# run the pipeline for 100 steps
+for i in range(100):
+    state = pipeline.step(state)
+
+print(monitor.get_min_fitness())

examples/jax_playground.py

@@ -1,27 +1,18 @@
-import numpy as np
-from jax import jit
+from functools import partial
 
-from configs import Configer
-from neat.pipeline import Pipeline
+import jax
+from jax import numpy as jnp, jit
 
-xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
-xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
-
-def main():
-    config = Configer.load_config("xor.ini")
-    print(config)
-    pipeline = Pipeline(config)
-    forward_func = pipeline.ask()
-    # inputs = np.tile(xor_inputs, (150, 1, 1))
-    outputs = forward_func(xor_inputs)
-    print(outputs)
+@partial(jit, static_argnames=['reverse'])
+def rank_element(array, reverse=False):
+    """
+    rank the element in the array.
+    if reverse is True, the rank is from large to small.
+    """
+    if reverse:
+        array = -array
+    return jnp.argsort(jnp.argsort(array))
 
 
-
-@jit
-def f(x, jit_config):
-    return x + jit_config["bias_mutate_rate"]
-
-
-if __name__ == '__main__':
-    main()
+a = jnp.array([1 ,5, 3, 5, 2, 1, 0])
+print(rank_element(a, reverse=True))

examples/jit_xor.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+from configs import Configer
+from jit_pipeline import Pipeline
+
+xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
+xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
+
+
+def evaluate(forward_func):
+    """
+    :param forward_func: (4: batch, 2: input size) -> (pop_size, 4: batch, 1: output size)
+    :return:
+    """
+    outs = forward_func(xor_inputs)
+    fitnesses = 4 - np.sum((outs - xor_outputs) ** 2, axis=(1, 2))
+    return np.array(fitnesses)  # returns a list
+
+
+def main():
+    config = Configer.load_config("xor.ini")
+    pipeline = Pipeline(config, seed=6)
+    nodes, cons = pipeline.auto_run(evaluate)
+    print(nodes, cons)
+
+
+if __name__ == '__main__':
+    main()

examples/xor.py

@@ -1,7 +1,7 @@
 import numpy as np
 
 from configs import Configer
-from neat.pipeline import Pipeline
+from pipeline import Pipeline
 
 xor_inputs = np.array([[0, 0], [0, 1], [1, 0], [1, 1]], dtype=np.float32)
 xor_outputs = np.array([[0], [1], [1], [0]], dtype=np.float32)
@@ -21,6 +21,8 @@ def main():
     config = Configer.load_config("xor.ini")
     pipeline = Pipeline(config, seed=6)
     nodes, cons = pipeline.auto_run(evaluate)
+    print(nodes, cons)
+
 
 if __name__ == '__main__':
     main()