ericminikel

require(rcdk) # chemical informatics functionality in R
require(gap)  # for qq plots later
options(stringsAsFactors=FALSE)
setwd('c:/sci/037cinf/analysis/sar/')

# plot molecules in R plot window instead of separate Java window
rcdkplot = function(molecule,width=500,height=500,marg=0,main='') {
    par(mar=c(marg,marg,marg,marg)) # set margins to zero since this isn't a real plot
    temp1 = view.image.2d(molecule,width,height) # get Java representation into an image matrix. set number of pixels you want horiz and vertical
    plot(NA,NA,xlim=c(1,10),ylim=c(1,10),xaxt='n',yaxt='n',xlab='',ylab='',main=main) # create an empty plot

ax3l

In general, check the crt/host_config.h file to find out which versions are supported. Sometimes it is possible to hack the requirements there to get some newer versions working, too :)

Thrust version can be found in $CUDA_ROOT/include/thrust/version.h.

Download Archives: https://developer.nvidia.com/cuda-toolkit-archive

Release notes for CUDA Toolkit (CTK):

• latest: https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html
• archive: https://docs.nvidia.com/cuda/archive/

0xnurl

Installing OpenFst Native Python Extension on MacOS

Starting from version 1.5, OpenFst has offered a native Python module, making the use of external wrappers like PyFst unnecessary. This has been greatly helpful since PyFst doesn't support Python 3.

1. Install OpenFst

• Download OpenFst 1.5.4 or above from http://openfst.cs.nyu.edu/twiki/bin/view/FST/FstDownload
• Unzip, untar: $ tar xzvf openfst-1.5.4.tar.gz
• $ ./configure --enable-python --enable-far

kenzotakahashi

import numpy as np
from sklearn.model_selection import train_test_split

import torch
import torch.nn as nn
from torch.autograd import Variable

np.random.seed(1337)

MAX_LEN = 30

naoko

# download
curl -O http://www.openfst.org/twiki/pub/FST/FstDownload/openfst-1.6.2.tar.gz
tar -xvzf openfst-1.6.2.tar.gz
rm openfst-1.6.2.tar.gz
cd openfst-1.6.2
./configure --enable-python
make
make install

# Test installtion

rwightman

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.modules.utils import _pair, _quadruple


class MedianPool2d(nn.Module):
    """ Median pool (usable as median filter when stride=1) module.
    

SLAPaper

'''Compares self-normalizing MLPs with regular MLPs.

Compares the performance of a simple MLP using two
different activation functions: RELU and SELU
on the Reuters newswire topic classification task.

# Reference:
  Klambauer, G., Unterthiner, T., Mayr, A., & Hochreiter, S. (2017).
  Self-Normalizing Neural Networks. arXiv preprint arXiv:1706.02515.
  https://arxiv.org/abs/1706.02515

jonathanBieler

module CMAES

    using Optim, Distributions, Parameters
    import Optim: ZerothOrderOptimizer, ZerothOrderState, initial_state, update_state!,
                  trace!, assess_convergence, AbstractOptimizerState, update!, value, value!, pick_best_x,
                  pick_best_f

    struct CMA <: Optim.ZerothOrderOptimizer
        λ::Int
        σ::Float64

HadrienG2

Hello, Rust community!

My name is Hadrien and I am a software performance engineer in a particle physics lab. My daily job is to figure out ways to make scientific software use hardware more efficiently without sacrificing its correctness, primarily by adapting old-ish codebases to the changes that occured in the software and computing landscape since the days where they were designed:

• CPU clock rates and instruction-level parallelism stopped going up, so optimizing code is now more important.
• Multi-core CPUs went from an exotic niche to a cheap commodity, so parallelism is not optional anymore.
• Core counts grow faster than RAM prices go down, so multi-processing is not enough anymore.
• SIMD vectors become wider and wider, so vectorization is not a gimmick anymore.

hypnopump

""" Dihedral angles from coordinates. Get dihedral angles from 
    protein backbone coordinates.
    This script takes 4 vectors representing 4 points as input
    and returns the dihedral angle between them.
    
    The script was originally developed to calculate dihedral angles (phi,psi)
    from protein backbone atoms' coordinates in 3D (N-term, C-alhpa, Cterm).
"""

import numpy as np