alxndrkalinin

import os
import cloudpickle
import multiprocessing as mp

class RunAsCUDASubprocess:
    def __init__(self, num_gpus=0, memory_fraction=0.8):
        self._num_gpus = num_gpus
        self._memory_fraction = memory_fraction

    @staticmethod

alxndrkalinin

import numpy as np
from keras.models import Model, Sequential
from keras.layers import Input, Dense, Activation
from keras.utils.data_utils import Sequence
from keras.losses import categorical_crossentropy
from keras.optimizers import SGD

class CustomSequence(Sequence):
    def __init__(self, x_set, y_set, batch_size):
        self.X,self.y = x_set,y_set

alxndrkalinin

BEGIN { FS=OFS="," }
{
    for (rowNr=1;rowNr<=NF;rowNr++) {
        cell[rowNr,NR] = $rowNr
    }
    maxRows = (NF > maxRows ? NF : maxRows)
    maxCols = NR
}
END {
    for (rowNr=1;rowNr<=maxRows;rowNr++) {

alxndrkalinin

#!/bin/bash

# call: "renameOffFilesByLst.sh norm/157"

runnum=${1##*/}
class=${1%/*}
destdir="${class}_off_named/$runnum"
listfile="0000${runnum}_c2.lst"
echo $listfile
liststring=$(cat $listfile |tr "\n" " ")

alxndrkalinin

module.exports = class BaseCtrl

  @register: (module, name) ->
    name ?= @name || @toString().match(/function\s*(.*?)\(/)?[1]
    module.controller name, @

  @inject: (annotations...) ->
    ANNOTATION_REG = /^(\S+)(\s+as\s+(\w+))?$/

    @annotations = annotations.map (annotation) ->

alxndrkalinin

#!/bin/bash

lines=1
while read -r line; do
	loniresults="/Users/alex/Projects/AtheyLab/LONI_Results/ShapeTranslator_2.ShapeOutputDirectory-"
	printf -v idx "%02d" $lines
	currdir=$loniresults$idx
	filename="/182_QEM_SubDiv.off"
	target=$currdir$filename
	destdir=${1%_c0.lst}

alxndrkalinin

for i in $(ls | egrep '*g000_mask-[[:digit:]]{3}\.nii.gz'); do
  m=${i%.nii.gz};
  echo $m;
  mm="$m-nucleoli-[[:digit:]]{4,10}\.nii.gz$";
  nn=$(ls | egrep $mm | wc -l);
  echo $nn;
done

alxndrkalinin

// Viterbi algorithm for finding hidden relationships
function Viterbi(data) {
    var V = [{}];
    var path = {};
 
    // Initialize base cases (t == 0)
    for(var i=0;i<data.states.length;i++) {
        var state = data.states[i];
        V[0][state] = data.start_probability[state] * data.emission_probability[state][data.observations[0]];
        path[state] = [state];