使用mdnn包使用Logistic回归进行预测是错误的

Question

我正在使用https://www.npmjs.com/package/mdnn包。

以下是我的代码：

var dnn = require('mdnn');
var x = [[3,5,0,5] ,[2,4,0,1] ,[2,2,0,3] ,[2,4,0,1] ,[2,4,0,1] ,[2,2,0,2] ,[3,4,0,1] ,[2,5,0,1] ,[2,4,0,1] ,[3,5,0,1] ,[2,4,0,1] ,[1,3,0,1] ,[2,5,0,2] ,[1,3,0,2] ,[1,3,0,2] ,[2,5,0,1] ,[3,3,0,2] ,[3,3,0,9] ,[1,4,0,1] ,[3,5,0,2] ,[2,4,0,1] ,[3,6,0,1] ,[3,2,0,1] ,[3,6,0,2] ,[2,3,0,1] ,[2,4,0,1] ,[2,5,0,2] ,[3,3,0,2] ,[3,4,0,1] ,[3,2,0,1] ,[3,4,0,1] ,[3,2,0,2] ,[3,5,0,2] ,[3,3,0,1] ,[2,3,0,2] ,[3,4,0,5] ,[3,4,0,1] ,[2,5,0,6] ,[1,5,0,1] ,[2,5,0,1] ,[3,3,0,1] ,[3,3,0,1] ,[1,2,0,3] ,[3,4,0,1] ,[2,4,0,1] ,[1,3,0,1] ,[3,7,0,1] ,[3,5,0,1] ,[2,2,0,3] ,[3,4,0,2] ,[3,6,0,1] ,[3,6,0,1] ,[3,4,0,2] ,[3,6,0,1] ,[5,4,0,1] ,[3,4,0,1] ,[1,2,0,1] ,[1,6,0,1] ,[3,4,0,2] ,[2,5,0,1] ,[3,6,0,1] ,[3,6,0,2] ,[5,2,0,1] ,[3,6,0,1] ,[3,6,0,2] ,[3,6,0,1] ,[3,6,0,1] ,[3,2,0,2] ,[5,6,0,2] ,[2,4,0,1] ,[3,5,0,1] ,[3,5,0,1] ,[3,4,0,1] ,[2,5,0,1] ,[3,6,0,1] ,[2,5,0,1] ,[3,4,0,1] ,[3,2,0,2] ,[2,4,0,1] ,[3,6,0,1] ,[3,6,0,1] ,[3,6,0,1] ,[3,7,0,1] ,[3,6,0,1] ,[3,6,0,1]];
var y = [ [993.6891666666667] ,[288.5808449074074] ,[993.6815393518518] ,[249.35396990740742] ,[251.90181712962962] ,[249.2097222222222] ,[142.1981712962963] ,[153.92697916666665] ,[993.4367245370371] ,[191.20947916666665] ,[169.03748842592591] ,[251.50061342592593] ,[251.47578703703704] ,[151.0910300925926] ,[251.45493055555556] ,[465.5181712962963] ,[993.1123842592592] ,[6116.8950115740745] ,[253.08877314814814] ,[993.0052662037037] ,[250.15501157407408] ,[141.4256712962963] ,[992.9504513888888] ,[141.70761574074075] ,[272.8670138888889] ,[154.1480439814815] ,[141.27747685185184] ,[250.72383101851852] ,[167.6178935185185] ,[141.27282407407407] ,[252.3682175925926] ,[1003.3935069444444] ,[1744.1734259259258] ,[152.5779861111111] ,[1003.2312731481481] ,[991.9828356481481] ,[1966.7281712962963] ,[152.90677083333333] ,[991.5500462962963] ,[140.08998842592592] ,[248.4744212962963] ,[251.15466435185186] ,[249.99975694444444] ,[171.9794212962963] ,[152.62525462962964] ,[1002.1122337962963] ,[186.07869212962962] ,[143.2667013888889] ,[810.1539351851852] ,[250.5128125] ,[150.63822916666666] ,[150.61777777777777] ,[164.0826851851852] ,[187.4275810185185] ,[271.48068287037034] ,[138.65458333333333] ,[1001.1144212962963] ,[138.58125] ,[150.11642361111112] ,[164.58778935185185] ,[999.7895833333333] ,[347.16716435185185] ,[248.02409722222222] ,[998.8821296296296] ,[998.881574074074] ,[998.8813541666667] ,[998.8810185185185] ,[136.67041666666665] ,[987.7227546296297] ,[998.6621527777778] ,[247.47444444444446] ,[359.71335648148147] ,[267.27574074074073] ,[987.2925578703704] ,[267.26530092592594] ,[247.4483449074074] ,[247.14627314814814] ,[139.12237268518518] ,[248.40618055555555] ,[997.935150462963] ,[135.72769675925926] ,[266.7886574074074] ,[248.3957638888889] ,[248.37237268518518] ,[152.8503472222222]];

var lrClassifier = new dnn.LogisticRegression({
    'input' : x,
    'label' : y,
    'n_in' : 4,
    'n_out' : 1
});

lrClassifier.set('log level',0); // 0 : nothing, 1 : info, 2 : warning.

var training_epochs = 900, lr = 1;

lrClassifier.train({
    'lr' : lr,
    'epochs' : training_epochs
});

x = [[3,5,0,5] ,[2,4,0,1]];

console.log("Result : ",lrClassifier.predict(x));

输出说：[[1]，[1]]