Andriy Makukha amakukha

## Phase1Contest2020.dyalog
P1←(1≠(×⊣))⌽↑{⊆⍺⍵}↓
P2←(128∘>∨191∘<)⊂⊢
P3←26⊥⎕A∘⍳
P4←≠⌿0=400 100 4∘.|⊢
P5←⊢{⍵,⍵-(××⍳∘|)-/⍺}⊃
P6←(⊣(⌿⍨)(+⌿=))⍪~⍨
P7←{⍺=2⊥∧/(2∘⊥⍣¯1)⍺⍵}
P8←¯1∧.=2×/(×2-/10∘⊥⍣¯1)
P9←{1∧.=≢∘∪¨⊆⍨2@(¯1∘=)×0~⍨(+\⍣¯1)⍵}
P10←↑((⊢⍴⍨(×/⍴))~((⊂' '⍴⍨(⍴⊃))))∘(↓(↑⍕¨))

## P9Phase2_2020.dyalog
⍝ PROBLEM 9.1, array-oriented solution, faster for the 3 examples provided.
Weights ← {
    (⎕IO ⎕ML ⎕WX) ← 0 1 3
    ⍝ Reads the mobile data from the file ⍵ and returns its weights.
    ⍝ Monadic function expecting a character vector and returning an integer vector.
    ⍝ Returns the weights ordered from left to right, top to bottom.

    ⍝ How it works:
    ⍝ We will build a square coefficient matrix where each variable is the weight of a left (┌) or right (┐) corner.
    ⍝ Let's say n is the number of leafs in the mobile; then n is also the number of pivots and by the lengths of the arms

## A PhD in CS, before, during, and after.md

      
              1 file
            
          
              1 fork
            
          
                0 comments
              
            
              3 stars
            
          
                Kronopath
                / A PhD in CS, before, during, and after.md
            
            
              Created
              April 15, 2016 00:41
            
          
    Speaker: Mike Walfish
mwalfish@nyu.edu
Reccomended reading: http://www.eecs.harvard.edu/htk/phdadvice/
Before

Should you do one?


## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

## 32.asm
; /usr/local/bin/nasm -f macho 32.asm && ld -macosx_version_min 10.7.0 -o 32 32.o && ./32

global start

section .text
start:
    push    dword msg.len
    push    dword msg
    push    dword 1
    mov     eax, 4

## brainfucc.c
/*
 * A simple, non-optimizing brainfuck to C translator.
 * 2010-08-31 - Version 1.0   (Cory Burgett)
 *
 * This code is hereby placed into the public domain.
 *
 * Originally located at: http://www4.ncsu.edu/~cmburget/brainfucc.c
 */

#include <stdio.h>
	P1←(1≠(×⊣))⌽↑{⊆⍺⍵}↓
	P2←(128∘>∨191∘<)⊂⊢
	P3←26⊥⎕A∘⍳
	P4←≠⌿0=400 100 4∘.\|⊢
	P5←⊢{⍵,⍵-(××⍳∘\|)-/⍺}⊃
	P6←(⊣(⌿⍨)(+⌿=))⍪~⍨
	P7←{⍺=2⊥∧/(2∘⊥⍣¯1)⍺⍵}
	P8←¯1∧.=2×/(×2-/10∘⊥⍣¯1)
	P9←{1∧.=≢∘∪¨⊆⍨2@(¯1∘=)×0~⍨(+\⍣¯1)⍵}
	P10←↑((⊢⍴⍨(×/⍴))~((⊂' '⍴⍨(⍴⊃))))∘(↓(↑⍕¨))
	⍝ PROBLEM 9.1, array-oriented solution, faster for the 3 examples provided.
	Weights ← {
	(⎕IO ⎕ML ⎕WX) ← 0 1 3
	⍝ Reads the mobile data from the file ⍵ and returns its weights.
	⍝ Monadic function expecting a character vector and returning an integer vector.
	⍝ Returns the weights ordered from left to right, top to bottom.

	⍝ How it works:
	⍝ We will build a square coefficient matrix where each variable is the weight of a left (┌) or right (┐) corner.
	⍝ Let's say n is the number of leafs in the mobile; then n is also the number of pivots and by the lengths of the arms
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)
	; /usr/local/bin/nasm -f macho 32.asm && ld -macosx_version_min 10.7.0 -o 32 32.o && ./32

	global start

	section .text
	start:
	push dword msg.len
	push dword msg
	push dword 1
	mov eax, 4
	/*
	* A simple, non-optimizing brainfuck to C translator.
	* 2010-08-31 - Version 1.0 (Cory Burgett)
	*
	* This code is hereby placed into the public domain.
	*
	* Originally located at: http://www4.ncsu.edu/~cmburget/brainfucc.c
	*/

	#include <stdio.h>