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Merge pull request #47 from gopherds/refactor
update example imports, update Dockerfile
2 parents 4b358a1 + 9e6046c commit b421ac1

8 files changed

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-182
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‎Dockerfile

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@@ -14,9 +14,9 @@ RUN mkdir -p "$GOPATH/src" "$GOPATH/bin" && chmod -R 777 "$GOPATH"
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# install gophernotes
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RUN go get golang.org/x/tools/cmd/goimports
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RUN go get github.com/gophergala2016/gophernotes
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RUN go get github.com/gopherds/gophernotes
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RUN mkdir -p ~/.ipython/kernels/gophernotes
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RUN cp -r $GOPATH/src/github.com/gophergala2016/gophernotes/kernel/* ~/.ipython/kernels/gophernotes
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RUN cp -r $GOPATH/src/github.com/gopherds/gophernotes/kernel/* ~/.ipython/kernels/gophernotes
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EXPOSE 8888
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CMD ["jupyter", "notebook"]

‎examples/Feed-Forward-Recurrent-NN.ipynb

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},
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"outputs": [],
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"source": [
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":import \"github.com/goml/gobrain\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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":import \"math/rand\""
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"import (\n",
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" \"math/rand\"\n",
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" \"github.com/goml/gobrain\"\n",
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")"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"metadata": {
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"collapsed": true
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},
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{
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"cell_type": "code",
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"metadata": {
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"collapsed": false
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},
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"}\n"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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"[1 1] -> [0.09740879532462095] : [0]\n"
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]
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},
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"execution_count": 6,
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 6,
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"collapsed": false
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"[1 1] -> [0.09740879532462095] : [0]\n"
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]
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},
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"execution_count": 7,
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"execution_count": 6,
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"metadata": {},
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}

‎examples/Pattern-Recognition.ipynb

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},
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"outputs": [],
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"source": [
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":import \"fmt\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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":import \"github.com/sjwhitworth/golearn/base\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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":import \"github.com/sjwhitworth/golearn/evaluation\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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":import \"github.com/sjwhitworth/golearn/knn\""
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"import (\n",
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" \"fmt\"\n",
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" \"github.com/sjwhitworth/golearn/base\"\n",
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" \"github.com/sjwhitworth/golearn/evaluation\"\n",
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" \"github.com/sjwhitworth/golearn/knn\"\n",
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")"
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]
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},
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{
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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@@ -224,13 +196,13 @@
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"Reference Class\tTrue Positives\tFalse Positives\tTrue Negatives\tPrecision\tRecall\tF1 Score\n",
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"---------------\t--------------\t---------------\t--------------\t---------\t------\t--------\n",
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"Iris-setosa\t30\t\t0\t\t58\t\t1.0000\t\t1.0000\t1.0000\n",
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"Iris-virginica\t27\t\t1\t\t58\t\t0.9643\t\t0.9310\t0.9474\n",
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"Iris-versicolor\t28\t\t2\t\t57\t\t0.9333\t\t0.9655\t0.9492\n",
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"Overall accuracy: 0.9659\n",
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"Iris-virginica\t28\t\t1\t\t58\t\t0.9655\t\t0.9655\t0.9655\n",
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"Iris-versicolor\t28\t\t1\t\t58\t\t0.9655\t\t0.9655\t0.9655\n",
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"Overall accuracy: 0.9773\n",
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"\n"
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]
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},
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -239,7 +211,7 @@
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"// Load in a dataset, with headers. Header attributes will be stored.\n",
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"// Think of instances as a Data Frame structure in R or Pandas.\n",
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"// You can also create instances from scratch.\n",
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"rawData, err := base.ParseCSVToInstances(\"datasets/iris.csv\", false)\n",
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"rawData, err := base.ParseCSVToInstances(\"iris.csv\", false)\n",
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"\n",
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"//Initialises a new KNN classifier\n",
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"cls := knn.NewKnnClassifier(\"euclidean\", 2)\n",

‎examples/Simple-Example.ipynb

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},
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"outputs": [],
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"source": [
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":import \"fmt\""
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"import \"fmt\""
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{
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{
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"data": {
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"text/plain": [
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"(\u001b[32mchan string\u001b[0m)(\u001b[34m\u001b[1m0xc820018120\u001b[0m)\n"
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"(\u001b[32mchan string\u001b[0m)(\u001b[34m\u001b[1m0xc4200180c0\u001b[0m)\n"
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"source": [
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"msg := <- messages"
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]
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{
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}
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"metadata": {

‎examples/Stateful-Goroutines.ipynb

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"source": [
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":import \"sync/atomic\""
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"outputs": [],
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"source": [
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":import \"time\""
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"import (\n",
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" \"fmt\"\n",
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" \"math/rand\"\n",
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" \"sync/atomic\"\n",
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" \"time\"\n",
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")"
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"text/plain": [
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"(chan *main.\u001b[32mwriteOp\u001b[0m)(\u001b[34m\u001b[1m0xc820018180\u001b[0m)\n"
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"(chan *main.\u001b[32mwriteOp\u001b[0m)(\u001b[34m\u001b[1m0xc420018120\u001b[0m)\n"
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"\u001b[34m\u001b[1m892297\u001b[0m\n"
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"output_type": "execute_result"
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}

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