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package org.deeplearning4j.eunice;
import org.apache.commons.math3.random.MersenneTwister;
import org.apache.commons.math3.random.RandomGenerator;
import org.deeplearning4j.datasets.fetchers.BaseDataFetcher;
import org.deeplearning4j.datasets.vectorizer.ImageVectorizer;
import org.deeplearning4j.datasets.vectorizer.Vectorizer;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.DataSet;
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tch, so far we have updates of size: 0 out of 4
INFO [2015-03-14 06:01:55,966] org.deeplearning4j.iterativereduce.actor.multilayer.MasterActor: Current jobs left [Job{workerId='localhost-39b19f91-7947-4e7c-ac7c-5623b9430cdf', work=null}, Job{workerId='localhost-32713b6c-95eb-4113-88d2-24dc10bea9cf', work=null}, Job{workerId='localhost-1ecd1364-f558-4975-a3ab-f43e4a347be6', work=null}, Job{workerId='localhost-636a3a50-289a-4148-93b7-d5f7cadcbfaf', work=null}]
INFO [2015-03-14 06:02:02,740] org.deeplearning4j.iterativereduce.actor.multilayer.ActorNetworkRunner: State tracker not done...blocking
INFO [2015-03-14 06:02:05,965] org.deeplearning4j.iterativereduce.actor.multilayer.MasterActor: Status check on next iteration
INFO [2015-03-14 06:02:05,966] org.deeplearning4j.iterativereduce.actor.multilayer.MasterActor: Still waiting on next batch, so far we have updates of size: 0 out of 4
INFO [2015-03-14 06:02:05,966] org.deeplearning4j.iterativereduce.actor.multilayer.MasterActor: Current jobs left [Job{worke
/usr/lib/jvm/java-1.7.0-openjdk-amd64/bin/java -Xmx8192m -Didea.launcher.port=7533 -Didea.launcher.bin.path=/usr/share/jetbrains/intellij-idea/bin -Dfile.encoding=UTF-8 -classpath /usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/rt.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/jce.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/compilefontconfig.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/charsets.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/jsse.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/rhino.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/management-agent.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/javazic.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/resources.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/ext/sunpkcs11.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/ext/icedtea-sound.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/ext/sunjce_provider.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/ext/localedata.jar:/usr/lib/jvm/java-1.7.0-openjdk-amd64/jre/lib/e
/usr/bin/python2.7 /home/keiron/.projects/lethality_prediction/python_files/wormbase_predict.py
Using Theano backend.
Loading the dataset...
- Loading the raw dataset from ../data/Worm_Dropshilla_Lethality.arff
- Vectorising the raw dataset into a format suitable for Neural Networks
- Randomly shuffling the dataset, to ensure proper results
- Splitting the dataset into separate training and testing sets
Now for the Deep Learning bit...
- Modelling the Neural Network
- Training the model
@KeironO
KeironO / gist:a2ce6d7fb7e7e10f616a51f511cb27b4
Created January 1, 2017 20:57
Periodic Table in JSON format
{"Ru": {"isotopic_weight": [95.907598, 97.905287, 98.9059393, 99.9042197, 100.9055822, 101.9043495, 103.90543], "atomic number": 44, "atomic_charge": 3, "isotopic_ratio": [0.0554, 0.0187, 0.1276, 0.126, 0.1706, 0.3155, 0.1862]}, "Re": {"isotopic_weight": [184.9529557, 186.9557508], "atomic number": 75, "atomic_charge": 2, "isotopic_ratio": [0.374, 0.626]}, "Rf": {"isotopic_weight": [261.0], "atomic number": 104, "atomic_charge": 0, "isotopic_ratio": [1.0]}, "Ra": {"isotopic_weight": [226.0], "atomic number": 88, "atomic_charge": 2, "isotopic_ratio": [1.0]}, "Rb": {"isotopic_weight": [84.9117893, 86.9091835], "atomic number": 37, "atomic_charge": 1, "isotopic_ratio": [0.7217, 0.2783]}, "Rn": {"isotopic_weight": [220.0], "atomic number": 86, "atomic_charge": 0, "isotopic_ratio": [1.0]}, "Rh": {"isotopic_weight": [102.905504], "atomic number": 45, "atomic_charge": 2, "isotopic_ratio": [1.0]}, "Be": {"isotopic_weight": [9.0121821], "atomic number": 4, "atomic_charge": 2, "isotopic_ratio": [1.0]}, "Ba": {"isotopic
{"Nigeria": {
"Kaduna": [
"Birni-Gwari",
"Chikun",
"Giwa",
"Igabi",
"Ikara",
"jaba",
"Jema'a",
"Kachia",
@KeironO
KeironO / kegg.json
Created March 27, 2017 22:32
KEGG Pathway Dictonary
{ "map00010" : "Glycolysis / Gluconeogenesis",
"map00020" : "Citrate cycle (TCA cycle)",
"map00030" : "Pentose phosphate pathway",
"map00040" : "Pentose and glucuronate interconversions",
"map00051" : "Fructose and mannose metabolism",
"map00052" : "Galactose metabolism",
"map00053" : "Ascorbate and aldarate metabolism",
"map00061" : "Fatty acid biosynthesis",
"map00062" : "Fatty acid elongation",
"map00071" : "Fatty acid degradation",

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@KeironO
KeironO / keybase.md
Created September 18, 2018 18:12
keybase.md

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  • I am KeironO on github.
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@KeironO
KeironO / curry.md
Last active March 22, 2020 05:51
Anti-capitalist liberal metropolitan elite vegetarian curry

Anti-capitalist liberal metropolitan elite vegetarian curry

Ingredients

Main bits and bobs

  • 3 chopped brown onions
  • 2 x 400g tins chickpeas, drained (ofc)
  • 2 tins of chopped tomatoes
  • 2 red peppers, deseeded and chopped
  • A decent amount of tomato puree.
  • A bit of fresh coriander (not needed, but it’s good to be a glutinous capitalistic pig sometimes)

For the paste