Speaker: Mike Walfish
Reccomended reading: http://www.eecs.harvard.edu/htk/phdadvice/
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 |
Speaker: Mike Walfish
Reccomended reading: http://www.eecs.harvard.edu/htk/phdadvice/
""" | |
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> |