GitHub - fieg/bayes: Implementation of Naive Bayes Classifier algorithm in PHP.You signed in with another tab or window. Reload to refresh your session. You signed out in another tab or window. Reload to refresh your session. You switched accounts on another tab or window. Reload to refresh your session. Dismiss alert
GitHub - gokceneraslan/awesome-deepbio: A curated list of awesome deep learning applications in the field of computational biology
A curated list of awesome deep learning applications in the field of computational biology 2007-08 | Fast model-based protein homology detection without alignment | Sepp Hochreiter, Martin Heusel, and Klaus Obermayer | Bioinformatics 2012-07 | Deep architectures for protein contact map prediction | Pietro Di Lena, Ken Nagata and Pierre Baldi Bioinformatics 2012-10 | Predicting protein residue–resi
sort: improve average quicksort performance · golang/go@6f6b2f0
- change way of protection from O(N^2) on duplicate values. Previous algorithm does additional comparisons and swaps on every split pass. Changed algorithm does one ordinal quicksort split pass, and if distribution is skewed, then additional pass to separate pivot's duplicates. Changed algorithm could be slower on very ununique slice, but it is still protected from O(N^2). - increase small slice s
Multikey quicksortとかTernary quicksortとか呼ばれるやつ．（参考： 続・アルゴリズムを学ぼう 4.7 接尾辞配列(SuffixArray)）
multikey_quick_sort.cpp P�L�=V @k��=V #include <iostream> #include <string> #include <vector> #include <algorithm> #include <assert.h> using namespace std; // pivotを選ぶ． char find_pivot_char(vector<string> &data, int left, int right, int depth) { assert(0 <= left); assert(right <= data.size()); assert(left + 1 <= right); char a = data[left].size() <= depth ? -1 : data[left][depth]; char b = data[(l
Quicksort in Swift
gistfile1.swift P��પ var randomNumbers = [42, 12, 88, 62, 63, 56, 1, 77, 88, 97, 97, 20, 45, 91, 62, 2, 15, 31, 59, 5] func partition(v: Int[], left: Int, right: Int) -> Int { var i = left for j in (left + 1)..(right + 1) { if v[j] < v[left] { i += 1 (v[i], v[j]) = (v[j], v[i]) } } (v[i], v[left]) = (v[left], v[i]) return i } func quicksort(v: Int[], left: Int, right: Int) { if right > left { let
GitHub - MickyDowns/deep-theano-rnn-lstm-car: Pack of Drones: Layered reinforcement learning (Q-learning w/ RNN) for complex "hunt" behaviors
Drone movement and coordination are learned thru five independently-trained neural networks in four categories of operation. Specifically: avoid: The first two neural networks enable the drone to avoid obstacles. The turn RNN trains a drone moving at constant speed to avoid stationary and moving obstacles. Inputs are a set of five sonar sensor readings that emanate from the front of the drone. Out
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