old_contents: from sklearn import svm
  from sklearn.datasets import load_iris
  
  from onl.nok.sklearn.Porter import port
  
  iris = load_iris()
  clf = svm.LinearSVC(C=1., random_state=0)
  clf.fit(iris.data, iris.target)
  
  # Cheese!
  
  print(port(clf, language='go'))
  
new_contents: from sklearn import svm
  from sklearn.datasets import load_iris
  
  from onl.nok.sklearn.Porter import port
  
  iris = load_iris()
  clf = svm.LinearSVC(C=1., random_state=0)
  clf.fit(iris.data, iris.target)
  
  # Cheese!
  
  print(port(clf, language='go'))
  
  """
  package main
  
  import (
  	"fmt"
  	"math"
  )
  
  func predict(atts []float64) int {
  
  	coefs := [][]float64{{0.18424209458473811, 0.45123000025163923, -0.80794587716737576, -0.45071660033253858}, {0.052877455748516447, -0.89214995228605254, 0.40398084459610972, -0.9376821661447452}, {-0.85070784319293802, -0.98670214922204336, 1.381010448739191, 1.8654095662423917}}
  	inters := []float64{0.10956266406702335, 1.6636707776739579, -1.7096109416521363}
  
  	if len(atts) != len(coefs[0]) {
  		return -1
  	}
  
  	classIdx := -1
  	classVal := math.Inf(-1)
  	outerCount, innerCount := len(coefs), len(coefs[0])
  	for i := 0; i < outerCount; i++ {
  		var prob float64
  		for j := 0; j < innerCount; j++ {
  			prob = prob + coefs[i][j]*atts[j]
  		}
  		if prob+inters[i] > classVal {
  			classVal = prob + inters[i]
  			classIdx = i
  		}
  	}
  	return classIdx
  }
  
  func main() {
  	atts := []float64{1, 2, 3, 4}
  	classIdx := predict(atts)
  	fmt.Println(classIdx)
  }
  """
  
subject: add transpiled go result to comment