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