mratsim

Figures for Laser wiki: https://github.com/numforge/laser/wiki/Convolution-optimisation-resources

mratsim

# -*- coding: utf-8 -*-
"""
Backprop as the method of Lagrange multiplers (and even the implicit function
theorem).
"""
from __future__ import division
import numpy as np
from arsenal.alphabet import Alphabet
from arsenal.math.checkgrad import finite_difference

mratsim

def make_input_seqs(data, seq_len, train_split=0.9):
    seq_len = seq_len + 1
    result = []
    for index in range(len(data) - seq_len):
        result.append(data[index: index + seq_len, :])
    result = np.array(result)
    train_ind = round(train_split * result.shape[0])
    train = result[:int(train_ind), :, :]
    x_train = train[:, :-1, :]
    y_train = train[:, -1, :]

mratsim

var a: array[2, uint64]

func toBinString*[N: static int](x: array[N, uint64]): string =

  const binChar = ['0', '1']
  const byte_size = N*8
  let bytes = cast[ptr array[byte_size, byte]](x.unsafeaddr)

  result = newStringOfCap(byte_size*8)

mratsim

# Copyright 2017-2018 Mamy André-Ratsimbazafy & the Arraymancer contributors
#
# Licensed 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
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

mratsim

# MIT License
# Copyright (c) 2018 Mamy André-Ratsimbazafy

## This files gives basic tensor library functionality, because yes we can
import strformat, macros, sequtils, random

type
  Tensor[Rank: static[int], T] = object
    ## Tensor data structure stored on Cpu
    ##   - ``shape``: Dimensions of the tensor

mratsim

import stint, math, algorithm

# config_big_384_29.h
# #define MODBYTES_384_29 48  	/**< Number of bytes in Modulus */
# #define BASEBITS_384_29 29 	/**< Numbers represented to base 2*BASEBITS */

# big_384_29.h
# #define BIGBITS_384_29 (8*MODBYTES_384_29) /**< Length in bits */
# #define NLEN_384_29 (1+((8*MODBYTES_384_29-1)/BASEBITS_384_29)) /**< length in bytes */

mratsim

# Nimbus
# Copyright (c) 2018 Status Research & Development GmbH
# Licensed under either of
#  * Apache License, version 2.0, ([LICENSE-APACHE](LICENSE-APACHE) or http://www.apache.org/licenses/LICENSE-2.0)
#  * MIT license ([LICENSE-MIT](LICENSE-MIT) or http://opensource.org/licenses/MIT)
# at your option. This file may not be copied, modified, or distributed except according to those terms.

import random, sequtils, strformat

type

mratsim

import ../stint/private/datatypes
import macros

var a: UintImpl[UintImpl[uint64]]

proc asWordsImpl(x: NimNode, current_path: NimNode, result: var NimNode) =
  ## Transforms an UintImpl/IntImpl into an array of words
  ## at compile-time. Recursve implementation.
  ## Result is from most significant word to least significant

mratsim

import
  stint,
  ../nimbus/constants,
  ../nimbus/vm/interpreter/[opcodes_impl, vm_forks],
  ../nimbus/vm/[message, computation, stack],
  ../nimbus/utils/header,
  ../nimbus/db/[db_chain, state_db, backends/memory_backend]

let msg = newMessage(
    to=ZERO_ADDRESS, #fixture{"exec"}{"address"}.getStr,