we have the sketchy conv net we promised see we deliver bet hackernew…

…s ppl wish they delivered on something rather then just tried and they'll be tryna claim they could've for the rest of their lives.

REAL DRAKE SHIT but serious listen to two birds one stone DRAKE is a genius with the diss

.gitignore

@@ -1,2 +1,3 @@
 .*.swp
 imgs
+dst.png

loader.go

@@ -12,16 +12,95 @@ TODO: don't be cheater and use python only golang pull request accepted
 
 import (
   "path/filepath"
+  "image"
   "image/png"
+  "math/rand"
+  "strings"
+  _"fmt"
   "os"
   "log"
+  _"github.com/disintegration/imaging"
+  "github.com/disintegration/gift"
+  "github.com/NOX73/go-neural"
+  "github.com/NOX73/go-neural/learn"
 )
 
+func randomArray(n int) []float32 {
+  ret := make([]float32, n)
+  for i := 0; i < n; i++ {
+    ret[i] = (rand.Float32()-0.5)*5
+  }
+  return ret
+}
 
 func main() {
   log.Print("use log so we don't have to put an underscore before the import")
 
+  type Example struct {
+    features []float64
+    yes bool
+  }
+
   paths := make(chan string)
+  processed := make(chan Example)
+
+  // the Seed for the network is 7
+  rand.Seed(7)
+
+  g := gift.New(
+    // edge detector
+    gift.Convolution(
+      []float32{
+        -1, -1, -1,
+        -1,  8, -1,
+        -1, -1, -1,
+      },
+      false, false, false, 0.0),
+    // is this max pool?
+    gift.Maximum(2, true),
+    gift.Resize(50, 0, gift.LinearResampling),
+
+    // random 5x5 conv, hmm but like the color channels bro this is a shit neural network
+    gift.Convolution(
+      randomArray(25),
+      false, false, false, 0.0),
+    // is this max pool?
+    gift.Maximum(2, true),
+    gift.Resize(25, 0, gift.LinearResampling),
+
+    // random 3x3 conv, hmm but like the color channels bro this is a shit neural network
+    gift.Convolution(
+      randomArray(9),
+      false, false, false, 0.0),
+    // is this max pool?
+    gift.Maximum(2, true),
+    gift.Resize(10, 0, gift.LinearResampling),
+
+    // 300 features one for each spartan RIP
+  )
+
+  n := neural.NewNetwork(300, []int{100,20,1})
+  n.RandomizeSynapses()
+
+  // forest builder
+  go func() {
+    // is this a proper design pattern?
+    // probs not it's awkward ROS node shit
+    for {
+      sample := <-processed
+      //fmt.Println(sample)
+
+      // ugh no inline if?
+      prob := []float64{0.01}
+      if sample.yes {
+        prob = []float64{0.4}
+      }
+
+      learn.Learn(n, sample.features, prob, 0.05)
+
+      println(learn.Evaluation(n, sample.features, prob))
+    }
+  }()
 
   // image loader and network runner 
   go func() {
@@ -36,13 +115,35 @@ func main() {
       if err != nil { log.Fatal(err) }
       f.Close()
 
-      println(img)
+			dst := image.NewRGBA(g.Bounds(img.Bounds()))
+      g.Draw(dst, img)
+
+      // extract features
+      // i can write much better than this wow shit
+      ret := make([]float64, 300)
+      cnt := 0
+      for i:=0;i<400;i++ {
+        if i%4 == 3 { continue }
+        ret[cnt] = float64(dst.Pix[i]) / 256.0
+        cnt += 1
+      }
+
+      //fmt.Println(path)
+
+      processed <- Example{features: ret, yes: strings.Contains(path, "street_signs")}
+
+      //imaging.Save(dst, "dst.png")
+      //println(dst)
     }
   }()
 
+	justone := true
   filepath.Walk("imgs/", func(path string, finfo os.FileInfo, err error) error {
     if finfo.IsDir() { return nil }
-    paths <- path
+		if justone {
+			paths <- path
+			justone = true
+		}
     return nil
   });