fsodogandji

from pytorch_lightning import LightningDataModule
from torch_geometric.datasets import TUDataset
from torch_geometric.data import DataLoader
from sklearn.model_selection import KFold


class ProteinsKFoldDataModule(LightningDataModule):
    def __init__(
            self,
            data_dir: str = "data/",

fsodogandji

fsodogandji

fsodogandji

TL;DR

• Guide from https://glitchwitch.io/blog/2018-09/windows-10-on-digitalocean/
• Download Windows ISO from https://www.microsoft.com/en-us/software-download/windows10ISO

Steps

• Create a build droplet with 4GB RAM
• Install QEMU and run the Windows 10 installation ISO

$ apt-get update && apt-get install qemu -y
$ qemu-img create -f raw windows10.img 16G

fsodogandji

[{
	"headers" : {
		"browser" : {
			"name" : "Chrome"
		},
		"os" : {
			"name" : "Windows"
		}
	},

fsodogandji

// Processing code by Etienne JACOB
// motion blur template by beesandbombs
// opensimplexnoise code in another tab might be necessary
// --> code here : https://gist.github.com/Bleuje/fce86ef35b66c4a2b6a469b27163591e

int[][] result;
float t, c;

float ease(float p) {
  return 3*p*p - 2*p*p*p;

fsodogandji

import pandas as pd
from collections import Counter
import tensorflow as tf
from tffm import TFFMRegressor
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np

# Loading datasets'

fsodogandji

"""
Implementations of:

    Probabilistic Matrix Factorization (PMF) [1],
    Bayesian PMF (BPMF) [2],
    Modified BPFM (mBPMF)

using `pymc3`. mBPMF is, to my knowledge, my own creation. It is an attempt
to circumvent the limitations of `pymc3` w/regards to the Wishart distribution:

fsodogandji

class StackedEnsembleClassifier(sklearn.base.BaseEstimator, sklearn.base.ClassifierMixin):
    """
    Ensemble of classifiers. Each classifier must have predict_proba and the head classifier is trained
    to predict the output based on the individual classifier outputs. Stratified k-fold cross-validation
    is used to get probabilities on held-out data.
    """
    def __init__(self, classifiers, head_classifier, num_folds=3):
        self.classifiers = classifiers
        self.head_classifier = head_classifier
        self.num_folds = num_folds

fsodogandji

# -*- coding: utf-8 -*-
"""
Created on Mon Aug 22 00:36:48 2016

@author: park
"""

#%% train

import theano