ajtulloch

import numpy as np
import numpy.linalg as la


class Kernel(object):
    """Implements list of kernels from
    http://en.wikipedia.org/wiki/Support_vector_machine
    """
    @staticmethod
    def linear():

ajtulloch

#! /usr/bin/env python

import pexpect
import pexpect.replwrap

repl = pexpect.replwrap.REPLWrapper("lua", u"> ", None, u"> ")

output = repl.run_command("= 1 + 1", timeout=1).splitlines()[1:]

assert(int(output[0]) == 2)

ajtulloch

diff --git a/tutorials/optimize/opt_gemm.py b/tutorials/optimize/opt_gemm.py
index 44ee53a7..c9785cbf 100644
--- a/tutorials/optimize/opt_gemm.py
+++ b/tutorials/optimize/opt_gemm.py
@@ -44,24 +44,24 @@ import timeit
 # The size of the matrix
 # (M, K) x (K, N)
 # You are free to try out different shapes, sometimes TVM optimization outperforms numpy with MKL.
-M = 1024
-K = 1024

ajtulloch

diff --git a/tutorials/optimize/opt_gemm.py b/tutorials/optimize/opt_gemm.py
index 44ee53a7..c9785cbf 100644
--- a/tutorials/optimize/opt_gemm.py
+++ b/tutorials/optimize/opt_gemm.py
@@ -44,24 +44,24 @@ import timeit
 # The size of the matrix
 # (M, K) x (K, N)
 # You are free to try out different shapes, sometimes TVM optimization outperforms numpy with MKL.
-M = 1024
-K = 1024

ajtulloch

@cython.boundscheck(False)
@cython.wraparound(False)
@cython.cdivision(True)
def _isotonic_regression(np.ndarray[DOUBLE, ndim=1] y,
                         np.ndarray[DOUBLE, ndim=1] weight,
                         np.ndarray[DOUBLE, ndim=1] solution):
    cdef:
        DOUBLE numerator, denominator
        Py_ssize_t i, pooled, n, k

ajtulloch

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You under the Apache License, Version 2.0
 * (the "License"); you may not use this file except in compliance with
 * the License.  You may obtain a copy of the License at
 *
 *    http://www.apache.org/licenses/LICENSE-2.0
 *

ajtulloch

def _isotonic_regression(np.ndarray[DOUBLE, ndim=1] y,
                         np.ndarray[DOUBLE, ndim=1] weight,
                         np.ndarray[DOUBLE, ndim=1] solution):

    cdef:
        Py_ssize_t current, i
        unsigned int len_active_set
        DOUBLE v, w

    len_active_set = y.shape[0]

ajtulloch

#include <memory>
#include <random>
#include <chrono>
#include <algorithm>
#include <iostream>

#include <gflags/gflags.h>
#include <boost/chrono/chrono.hpp>
#include <boost/chrono/duration.hpp>
#include <boost/chrono/process_cpu_clocks.hpp>

ajtulloch

#! /bin/bash

# A hacky script that converts org-mode documents to an old-style
# Remarkup dialect, used by Phabricator and other tools.
set -x
set -e

pandoc --read=org --write=markdown --atx-headers $1 \
    | sed -E "s/\{\.(.*)\}/lang=\1/g" \
    | sed -E "s/\[(.*)\]\((.*)\)/[[\2 | \1]]/g" \

ajtulloch

--------------------8<--------------------
Final stats (running with 5 nodes for 30s):
Bytes read: 3.35MB
Bytes written: 3.30MB
Connection attempts: 806
Correct queries: 726

Score breakdown:
7260 points from queries
-1321 points from network traffic