| """ | |
| 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) |
| import os | |
| import numpy as np | |
| from matplotlib import pyplot as plt | |
| from time import time | |
| from foxhound import activations | |
| from foxhound import updates | |
| from foxhound import inits | |
| from foxhound.theano_utils import floatX, sharedX |
(Codecs are extracted from https://web.archive.org/web/20120722124832/http://opencv.willowgarage.com/wiki/QuickTimeCodecs )
| { | |
| "settings": { | |
| "icons" : true | |
| }, | |
| "firstname": "Prateek", | |
| "familyname": "Agarwal", | |
| "linkedin_id": "prat0318", | |
| "github_id": "prat0318", | |
| "bio_data": { | |
| "email": "prat0318 @ cs.utexas.edu", |
| function [Theta] = glasso(S,rho) | |
| [n,p] = size(S); | |
| max_iterations = 100; | |
| t = 1e-4; | |
| convergence_value = t * meanabs(S - diag(diag(S))); | |
| % initialise | |
| W_old = S + rho*eye(p); | |
| W = W_old; |
| """Example of adaptive Lasso to produce event sparser solutions | |
| Adaptive lasso consists in computing many Lasso with feature | |
| reweighting. It's also known as iterated L1. | |
| """ | |
| # Authors: Alexandre Gramfort <[email protected]> | |
| # | |
| # License: BSD (3-clause) | |
| import numpy as np |
| typedef struct circular_buffer | |
| { | |
| void *buffer; // data buffer | |
| void *buffer_end; // end of data buffer | |
| size_t capacity; // maximum number of items in the buffer | |
| size_t count; // number of items in the buffer | |
| size_t sz; // size of each item in the buffer | |
| void *head; // pointer to head | |
| void *tail; // pointer to tail | |
| } circular_buffer; |