inoryy

import tensorflow as tf


class TfCategorical:
    def __init__(self, logits):
        self.logits = logits
        self.probs = tf.nn.softmax(logits)

    def sample(self):
        u = tf.random_uniform(tf.shape(self.logits))

inoryy

#!/home/inoryy/anaconda3/bin/python

import sys, time
from math import sqrt, acos, pi, cos, sin
from decimal import Decimal, ROUND_HALF_UP

class Point(object):
    __slots__ = 'x', 'y'

    def __init__(self, x, y):

inoryy

Node* mcts(Node* root) {
    save();
    while (true) {
        if (TIME >= TIME_LIMIT) break;

        // select
        Node* node = root;
        while (!isTerminal() && node->isExpanded()) {
            node = node->uctChild();
            apply(node->action);

inoryy

from random import random
from math import log, cos, sin, sqrt, pi

# Implementation of Box-Muller transform to
# generate two random numbers from N(mu, sigma) distribution
def pnorm(mu = 0, sigma = 1):
    r = sqrt(-2*log(random()))
    a = 2*pi*random()
    return sigma*r*cos(a) + mu, sigma*r*sin(a) + mu

inoryy

#!/usr/bin/env python

import sys, re, os

DEFAULT_MAIN = 'main.cpp'
DEFAULT_OUT  = 'arena.cpp'
BUNDLE_SYM   = '// ###'

def bundle(flname, main = False):
    base = os.path.dirname(flname)

inoryy

Fixing MKL on AMD Zen CPU

As per discussion on Reddit, it seems a workaround for the Intel MKL's notorious SIMD throttling of AMD Zen CPUs is as simple a setting MKL_DEBUG_CPU_TYPE=5 environment variable.

Benchmarks

All three scripts are executed in the same Python 3.7 environment on a first-gen AMD Zen CPU (1950x).
The difference will be even bigger on newer models as first-gen Zen resolves 256-bit AVX2 in two 128-bit instructions.