jam/test/test-ice3.js

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    "swidth": 2.8,
    "plength": 5.1,
    "pwidth": 1.5,
    "variety": "Virginica"
  },
  {
    "slength": 6.1,
    "swidth": 2.6,
    "plength": 5.6,
    "pwidth": 1.4,
    "variety": "Virginica"
  },
  {
    "slength": 7.7,
    "swidth": 3.0,
    "plength": 6.1,
    "pwidth": 2.3,
    "variety": "Virginica"
  },
  {
    "slength": 6.3,
    "swidth": 3.4,
    "plength": 5.6,
    "pwidth": 2.4,
    "variety": "Virginica"
  },
  {
    "slength": 6.4,
    "swidth": 3.1,
    "plength": 5.5,
    "pwidth": 1.8,
    "variety": "Virginica"
  },
  {
    "slength": 6.0,
    "swidth": 3.0,
    "plength": 4.8,
    "pwidth": 1.8,
    "variety": "Virginica"
  },
  {
    "slength": 6.9,
    "swidth": 3.1,
    "plength": 5.4,
    "pwidth": 2.1,
    "variety": "Virginica"
  },
  {
    "slength": 6.7,
    "swidth": 3.1,
    "plength": 5.6,
    "pwidth": 2.4,
    "variety": "Virginica"
  },
  {
    "slength": 6.9,
    "swidth": 3.1,
    "plength": 5.1,
    "pwidth": 2.3,
    "variety": "Virginica"
  },
  {
    "slength": 5.8,
    "swidth": 2.7,
    "plength": 5.1,
    "pwidth": 1.9,
    "variety": "Virginica"
  },
  {
    "slength": 6.8,
    "swidth": 3.2,
    "plength": 5.9,
    "pwidth": 2.3,
    "variety": "Virginica"
  },
  {
    "slength": 6.7,
    "swidth": 3.3,
    "plength": 5.7,
    "pwidth": 2.5,
    "variety": "Virginica"
  },
  {
    "slength": 6.7,
    "swidth": 3.0,
    "plength": 5.2,
    "pwidth": 2.3,
    "variety": "Virginica"
  },
  {
    "slength": 6.3,
    "swidth": 2.5,
    "plength": 5.0,
    "pwidth": 1.9,
    "variety": "Virginica"
  },
  {
    "slength": 6.5,
    "swidth": 3.0,
    "plength": 5.2,
    "pwidth": 2.0,
    "variety": "Virginica"
  }
  
 ]
 
var target = "variety";
var features = ["slength", "swidth","plength","pwidth"];
var model = ml.learn({
 algorithm:ml.ML.ICE,
 data:training_data,
 target:target,
 features:features,
 eps:0.1
})

print(ml.classify(model,training_data).map(function (row,index) {
  row.y=training_data[index][target]
  return row;
}))
print(ml.print(model).split('\n').length)