1 files changed, 21 insertions, 6 deletions
diff --git a/app/client/modules/samplernn/views/samplernn.graph.js b/app/client/modules/samplernn/views/samplernn.graph.js
index 40e47fa..58f8d02 100644
--- a/app/client/modules/samplernn/views/samplernn.graph.js
+++ b/app/client/modules/samplernn/views/samplernn.graph.js
@@ -18,7 +18,7 @@ import TextInput from '../../../common/textInput.component'
 class SampleRNNGraph extends Component {
   constructor(props){
     super()
-    props.actions.load_loss()
+    props.actions.load_graph()
   }
   render(){
     this.refs = {}
@@ -32,8 +32,8 @@ class SampleRNNGraph extends Component {
     )
   }
   componentDidUpdate(){
-    const { lossReport } = this.props.samplernn
-    if (! lossReport) return
+    const { lossReport, results } = this.props.samplernn
+    if (! lossReport || ! results) return
     const canvas = this.refs.canvas
     canvas.width = window.innerWidth
     canvas.height = window.innerHeight
@@ -45,7 +45,16 @@ class SampleRNNGraph extends Component {
     const h = canvas.height
     ctx.clearRect(0,0,w,h)
 
-    const keys = Object.keys(lossReport).sort().filter(k => !!lossReport[k].length)
+    const resultsByDate = results.map(file => {
+      if (!file.name.match(/^exp:/)) return null
+      const dataset = file.name.split("-")[3].split(":")[1]
+      return [
+        file.date,
+        dataset
+      ]
+    }).filter(a => !!a).sort((a,b) => a[0]-a[1])
+
+    const keys = Object.keys(lossReport).filter(k => !!lossReport[k].length)
     let scaleMax = 0
     let scaleMin = Infinity
     let epochsMax = 0
@@ -115,13 +124,19 @@ class SampleRNNGraph extends Component {
     ctx.lineWidth = 1
     ctx.restore()
 
-    keys.forEach(key => {
+    const min_date = resultsByDate[0][0]
+    const max_date = resultsByDate[resultsByDate.length-1][0]
+    resultsByDate.forEach(pair => {
+      const date = pair[0]
+      const key = pair[1]
       const loss = lossReport[key]
+      if (!key) return
       const vf = parseFloat(loss[loss.length-1].training_loss) || 0
       const vg = parseFloat(loss[0].training_loss) || 5
       // console.log(vf)
       const vv = 1 - norm(vf, scaleMin, scaleMax/2)
-      ctx.lineWidth = (1-norm(vf, scaleMin, scaleMax)) * 4
+      // ctx.lineWidth = (1-norm(vf, scaleMin, scaleMax)) * 4
+      ctx.lineWidth = norm(date, min_date, max_date) * 4
       ctx.strokeStyle = 'rgba(' + [randrange(30,190), randrange(30,150), randrange(60,120)].join(',') + ',' + 0.8+ ')'
       let begun = false
       loss.forEach((a, i) => {