change repo structure; modify readme

tensorneat/utils/tools.py

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+from functools import partial
+
+import numpy as np
+import jax
+from jax import numpy as jnp, Array, jit, vmap
+
+I_INT = np.iinfo(jnp.int32).max  # infinite int
+
+
+def unflatten_conns(nodes, conns):
+    """
+    transform the (C, CL) connections to (CL-2, N, N), 2 is for the input index and output index)
+    :return:
+    """
+    N = nodes.shape[0]
+    CL = conns.shape[1]
+    node_keys = nodes[:, 0]
+    i_keys, o_keys = conns[:, 0], conns[:, 1]
+    i_idxs = vmap(key_to_indices, in_axes=(0, None))(i_keys, node_keys)
+    o_idxs = vmap(key_to_indices, in_axes=(0, None))(o_keys, node_keys)
+    res = jnp.full((CL - 2, N, N), jnp.nan)
+
+    # Is interesting that jax use clip when attach data in array
+    # however, it will do nothing set values in an array
+    # put all attributes include enable in res
+    res = res.at[:, i_idxs, o_idxs].set(conns[:, 2:].T)
+
+    return res
+
+
+def key_to_indices(key, keys):
+    return fetch_first(key == keys)
+
+
+@jit
+def fetch_first(mask, default=I_INT) -> Array:
+    """
+    fetch the first True index
+    :param mask: array of bool
+    :param default: the default value if no element satisfying the condition
+    :return: the index of the first element satisfying the condition. if no element satisfying the condition, return default value
+    """
+    idx = jnp.argmax(mask)
+    return jnp.where(mask[idx], idx, default)
+
+
+@jit
+def fetch_random(rand_key, mask, default=I_INT) -> Array:
+    """
+    similar to fetch_first, but fetch a random True index
+    """
+    true_cnt = jnp.sum(mask)
+    cumsum = jnp.cumsum(mask)
+    target = jax.random.randint(rand_key, shape=(), minval=1, maxval=true_cnt + 1)
+    mask = jnp.where(true_cnt == 0, False, cumsum >= target)
+    return fetch_first(mask, default)
+
+
+@partial(jit, static_argnames=['reverse'])
+def rank_elements(array, reverse=False):
+    """
+    rank the element in the array.
+    if reverse is True, the rank is from small to large. default large to small
+    """
+    if not reverse:
+        array = -array
+    return jnp.argsort(jnp.argsort(array))
+
+
+@jit
+def mutate_float(key, val, init_mean, init_std, mutate_power, mutate_rate, replace_rate):
+    k1, k2, k3 = jax.random.split(key, num=3)
+    noise = jax.random.normal(k1, ()) * mutate_power
+    replace = jax.random.normal(k2, ()) * init_std + init_mean
+    r = jax.random.uniform(k3, ())
+
+    val = jnp.where(
+        r < mutate_rate,
+        val + noise,
+        jnp.where(
+            (mutate_rate < r) & (r < mutate_rate + replace_rate),
+            replace,
+            val
+        )
+    )
+
+    return val
+
+
+@jit
+def mutate_int(key, val, options, replace_rate):
+    k1, k2 = jax.random.split(key, num=2)
+    r = jax.random.uniform(k1, ())
+
+    val = jnp.where(
+        r < replace_rate,
+        jax.random.choice(k2, options),
+        val
+    )
+
+    return val
+
+def argmin_with_mask(arr, mask):
+    masked_arr = jnp.where(mask, arr, jnp.inf)
+    min_idx = jnp.argmin(masked_arr)
+    return min_idx