/
basics.py
99 lines (89 loc) · 2.87 KB
/
basics.py
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# -*- coding: utf-8 -*-
from sklearn.tree import tree
from sklearn.datasets import load_iris
from sklearn_porter import Porter
iris_data = load_iris()
X = iris_data.data
y = iris_data.target
clf = tree.DecisionTreeClassifier()
clf.fit(X, y)
porter = Porter(clf)
output = porter.export()
print(output)
"""
class Brain {
public static int predict(float[] atts) {
if (atts.length != 4) { return -1; }
int[] classes = new int[3];
if (atts[2] <= 2.4500000476837158) {
classes[0] = 50;
classes[1] = 0;
classes[2] = 0;
} else {
if (atts[3] <= 1.75) {
if (atts[2] <= 4.9499998092651367) {
if (atts[3] <= 1.6500000953674316) {
classes[0] = 0;
classes[1] = 47;
classes[2] = 0;
} else {
classes[0] = 0;
classes[1] = 0;
classes[2] = 1;
}
} else {
if (atts[3] <= 1.5499999523162842) {
classes[0] = 0;
classes[1] = 0;
classes[2] = 3;
} else {
if (atts[2] <= 5.4499998092651367) {
classes[0] = 0;
classes[1] = 2;
classes[2] = 0;
} else {
classes[0] = 0;
classes[1] = 0;
classes[2] = 1;
}
}
}
} else {
if (atts[2] <= 4.8500003814697266) {
if (atts[0] <= 5.9499998092651367) {
classes[0] = 0;
classes[1] = 1;
classes[2] = 0;
} else {
classes[0] = 0;
classes[1] = 0;
classes[2] = 2;
}
} else {
classes[0] = 0;
classes[1] = 0;
classes[2] = 43;
}
}
}
int class_idx = 0;
int class_val = classes[0];
for (int i = 1; i < 3; i++) {
if (classes[i] > class_val) {
class_idx = i;
class_val = classes[i];
}
}
return class_idx;
}
public static void main(String[] args) {
if (args.length == 4) {
float[] atts = new float[args.length];
for (int i = 0, l = args.length; i < l; i++) {
atts[i] = Float.parseFloat(args[i]);
}
System.out.println(Brain.predict(atts));
}
}
}
"""