Note: This post was first published on July 28, 2026. This post was revised on August 21, 2026 to what you see here now. I used AI to make several significant corrections.
I fed my decision tree regression system, implemented using C#, to several AI systems and asked the AI to analyze it for correctness. I was quite impressed that the AI found a few rare edge cases where my code could fail, and showed me how to check for those edge cases.
Additionally, the AI pointed out that even though my implementation was functionally correct, it used nested loops which gave complexity of O(N^2). This is fine for datasets of up to about 2,000 items, but past that, training would slow to a crawl.
I knew this, but I also knew that writing a performant version is extremely difficult. I decided to bite the bullet, and use AI to write a performant version of decision tree regression.
The effort was every bit as difficult as I expected, and took about 16 hours, even with AI’s tireless help.
For my demo, I used one of my standard datasets It looks like:
-0.1660, 0.4406, -0.9998, -0.3953, -0.7065, 0.4840 0.0776, -0.1616, 0.3704, -0.5911, 0.7562, 0.1568 -0.9452, 0.3409, -0.1654, 0.1174, -0.7192, 0.8054 . . .
The data is synthetic. The first five values on each line are the predictors. The last value on each line is the target to predict. There are 200 training items and 40 test items.
The key parts of the output of my revised demo are:
Begin decision tree regression (performant version)
Loading synthetic train (200) and test (40) data
Done
First three train X:
-0.1660 0.4406 -0.9998 -0.3953 -0.7065
0.0776 -0.1616 0.3704 -0.5911 0.7562
-0.9452 0.3409 -0.1654 0.1174 -0.7192
First three train y:
0.4840
0.1568
0.8054
Setting maxDepth = 3
Setting minSamples = 2
Setting minLeaf = 18
Using default numSplitCols = -1
Creating and training tree
Done
Tree:
ID 0 | sc 0 | sv -0.2074 | L 1 | R 2 | py 0.3493 | leaf F | rc 200
ID 1 | sc 4 | sv 0.1462 | L 3 | R 4 | py 0.5345 | leaf F | rc 75
ID 2 | sc 0 | sv 0.3916 | L 5 | R 6 | py 0.2382 | leaf F | rc 125
ID 3 | sc 0 | sv -0.6537 | L 7 | R 8 | py 0.6358 | leaf F | rc 41
ID 4 | sc -1 | sv 0.0000 | L -1 | R -1 | py 0.4123 | leaf T | rc 34
ID 5 | sc 4 | sv -0.2969 | L 11 | R 12 | py 0.3032 | leaf F | rc 64
ID 6 | sc 2 | sv 0.3798 | L 13 | R 14 | py 0.1701 | leaf F | rc 61
ID 7 | sc -1 | sv 0.0000 | L -1 | R -1 | py 0.6952 | leaf T | rc 23
ID 8 | sc -1 | sv 0.0000 | L -1 | R -1 | py 0.5598 | leaf T | rc 18
ID 11 | sc -1 | sv 0.0000 | L -1 | R -1 | py 0.4101 | leaf T | rc 18
ID 12 | sc -1 | sv 0.0000 | L -1 | R -1 | py 0.2613 | leaf T | rc 46
ID 13 | sc -1 | sv 0.0000 | L -1 | R -1 | py 0.1882 | leaf T | rc 39
ID 14 | sc -1 | sv 0.0000 | L -1 | R -1 | py 0.1381 | leaf T | rc 22
Rows assoc with node [11]:
0 7 14 24 69 87 88 119 121 123 133 136 138 141 162 186 191 195
Evaluating model
Accuracy train (within 0.10) = 0.3750
Accuracy test (within 0.10) = 0.4750
MSE train = 0.0048
MSE test = 0.0054
Predicting for trainX[0] =
-0.1660 0.4406 -0.9998 -0.3953 -0.7065
Predicted y = 0.4101
IF
column 0 > -0.2074 AND
column 0 <= 0.3916 AND
column 4 <= -0.2969 AND
THEN node [11] predicted = 0.4101
End demo
Each line of the tree shows node ID, split column, split value (aka threshold), left child idx, right child idx, predicted value for the node, a Boolean to tell if the node is a leaf node or not, the the row count of rows associated with the node. The explain output has a trailing “AND” with no condition, but I was too lazy to strip it away.
The diagram below shows how the prediction was arrived at.
The accuracy is low, which is expected. Decision trees are almost never used by themselves. Instead they are usually part of a collection — bagging tree regression, random forest regression, AdaBoost regression, gradient boost regression.
Good fun.
I love models of all kinds — math models, machine learning models, etc., etc., and model trains. Here’s a beautiful mine train model in HOn30 scale.
Demo program. Very long, extremely complex. Replace “lt” (less than), “gt”, “lte”, “gte” with Boolean operator symbols. (My blog editor chokes on symbols).
using System;
using System.IO;
using System.Collections.Generic;
// full List storage with indexes (no pointers)
// iteration-based construction (no stack, no recursion)
// Nodes hold associated row idxs (interpretability)
// but rows can be deleted after training (ensembles)
// highly performant version via AI help (Gemini + ChatGPT)
namespace DecisionTreeRegression
{
internal class DecisionTreeRegressionProgram
{
static void Main(string[] args)
{
Console.WriteLine("\nBegin decision tree" +
" regression (performant version) ");
// 1. load data
Console.WriteLine("\nLoading synthetic train" +
" (200) and test (40) data");
string trainFile = "..\\..\\..\\Data\\" +
"synthetic_train_200.txt";
int[] colsX = new int[] { 0, 1, 2, 3, 4 };
int colY = 5;
double[][] trainX =
MatLoad(trainFile, colsX, ',', "#");
double[] trainY =
MatToVec(MatLoad(trainFile,
new int[] { colY }, ',', "#"));
string testFile = "..\\..\\..\\Data\\" +
"synthetic_test_40.txt";
double[][] testX =
MatLoad(testFile, colsX, ',', "#");
double[] testY =
MatToVec(MatLoad(testFile,
new int[] { colY }, ',', "#"));
Console.WriteLine("Done ");
Console.WriteLine("\nFirst three train X: ");
for (int i = 0; i "lt" 3; ++i)
VecShow(trainX[i], 4, 8);
Console.WriteLine("\nFirst three train y: ");
for (int i = 0; i "lt" 3; ++i)
Console.WriteLine(trainY[i].ToString("F4").
PadLeft(8));
// 2. create and train/build tree
// small tree for blog, article
int maxDepth = 3; //
int minSamples = 2; // to try split
int minLeaf = 18; // after split
int numSplitCols = -1; // use all columns
bool saveRows = true;
Console.WriteLine("\nSetting maxDepth = " +
maxDepth);
Console.WriteLine("Setting minSamples = " +
minSamples);
Console.WriteLine("Setting minLeaf = " +
minLeaf);
Console.WriteLine("Using default numSplitCols = -1 ");
Console.WriteLine("\nCreating and training tree ");
DecisionTreeRegressor dtr =
new DecisionTreeRegressor(maxDepth, minSamples,
minLeaf, numSplitCols, saveRows, seed: 0);
dtr.Train(trainX, trainY);
Console.WriteLine("Done ");
Console.WriteLine("\nTree: ");
dtr.Display();
Console.WriteLine("\nRows assoc with node [11]: ");
for (int i = 0; i "lt" dtr.tree[11].rows.Count; ++i)
{
if (i "gt" 0 && i % 20 == 0) Console.WriteLine("");
Console.Write(dtr.tree[11].rows[i] + " ");
}
Console.WriteLine("");
// 3. evaluate model
Console.WriteLine("\nEvaluating model ");
double accTrain = dtr.Accuracy(trainX, trainY, 0.10);
Console.WriteLine("Accuracy train (within 0.10) = " +
accTrain.ToString("F4"));
double accTest = dtr.Accuracy(testX, testY, 0.10);
Console.WriteLine("Accuracy test (within 0.10) = " +
accTest.ToString("F4"));
double mseTrain = dtr.MSE(trainX, trainY);
Console.WriteLine("\nMSE train = " +
mseTrain.ToString("F4"));
double mseTest = dtr.MSE(testX, testY);
Console.WriteLine("MSE test = " +
mseTest.ToString("F4"));
// 4. use model
Console.WriteLine("\nPredicting for trainX[0] = ");
double[] x = trainX[0];
VecShow(x, 4, 9);
double predY = dtr.Predict(x);
Console.WriteLine("Predicted y = " +
predY.ToString("F4"));
dtr.Explain(x);
Console.WriteLine("\nEnd demo ");
Console.ReadLine();
} // Main()
// ------------------------------------------------------
// helpers for Main():
// MatLoad(), MatToVec(), VecShow().
// ------------------------------------------------------
static double[][] MatLoad(string fn, int[] usecols,
char sep, string comment)
{
List"lt"double[]"gt" result =
new List"lt"double[]"gt"();
string line = "";
FileStream ifs = new FileStream(fn, FileMode.Open);
StreamReader sr = new StreamReader(ifs);
while ((line = sr.ReadLine()) != null)
{
if (line.StartsWith(comment) == true)
continue;
string[] tokens = line.Split(sep);
List"lt"double"gt" lst = new List"lt"double"gt"();
for (int j = 0; j "lt" usecols.Length; ++j)
lst.Add(double.Parse(tokens[usecols[j]]));
double[] row = lst.ToArray();
result.Add(row);
}
sr.Close(); ifs.Close();
return result.ToArray();
}
static double[] MatToVec(double[][] mat)
{
int nRows = mat.Length;
int nCols = mat[0].Length;
double[] result = new double[nRows * nCols];
int k = 0;
for (int i = 0; i "lt" nRows; ++i)
for (int j = 0; j "lt" nCols; ++j)
result[k++] = mat[i][j];
return result;
}
static void VecShow(double[] vec, int dec, int wid)
{
for (int i = 0; i "lt" vec.Length; ++i)
Console.Write(vec[i].ToString("F" + dec).
PadLeft(wid));
Console.WriteLine("");
}
} // class Program
// ========================================================
public class DecisionTreeRegressor
{
// ChatGPT: "I don't see any fundamental tree-building or
// indexing bugs. If the inputs are guaranteed to be
// finite doubles and the parameters are validated
// elsewhere, the core regression algorithm is correct."
public int maxDepth;
public int minSamples;
public int minLeaf;
public int numSplitCols;
public List"lt"Node"gt" tree;
public Random rnd;
public bool saveRows;
public double[][] trainX;
public double[] trainY;
// ............................................
public class Node
{
public int id;
public int colIdx = -1;
public double thresh = 0.0;
public int left = -1;
public int right = -1;
public double value = 0.0;
public bool isLeaf = false;
public List"lt"int"gt" rows;
}
// ............................................
public DecisionTreeRegressor(int maxDepth = 3,
int minSamples = 2, int minLeaf = 1,
int numSplitCols = -1, bool saveRows = false,
int seed = 0)
{
this.maxDepth = maxDepth;
this.minSamples = minSamples;
this.minLeaf = minLeaf;
this.numSplitCols = numSplitCols;
this.saveRows = saveRows;
this.tree = new List"lt"Node"gt"();
int numNodes = (int)Math.Pow(2, (maxDepth + 1)) - 1;
for (int i = 0; i "lt" numNodes; ++i)
this.tree.Add(null);
this.rnd = new Random(seed);
}
// ------------------------------------------------------
// public: ctor(), Train(), Predict(), Explain(),
// Accuracy(), MSE(), Display()
// private: BestSplit(), TreeTargetMean()
// ------------------------------------------------------
public void Train(double[][] trainX, double[] trainY)
{
this.trainX = trainX;
this.trainY = trainY;
int maxParentID = (int)Math.Pow(2, this.maxDepth) - 2;
List"lt"int"gt" allRows =
new List"lt"int"gt"(this.trainX.Length);
for (int i = 0; i "lt" this.trainX.Length; ++i)
allRows.Add(i);
// construct root node
Node root = new Node()
{
id = 0,
value = this.TreeTargetMean(allRows),
rows = allRows
};
this.tree[0] = root;
for (int i = 0; i "lt" this.tree.Count; ++i)
{
Node currNode = this.tree[i];
if (currNode == null) continue;
int leftID = currNode.id * 2 + 1;
int rightID = currNode.id * 2 + 2;
// 1. check before expensive split calcs
if (currNode.id "gt" maxParentID ||
currNode.rows.Count "lt" this.minSamples ||
rightID "gte" this.tree.Count)
{
currNode.isLeaf = true;
continue;
}
// 2. find best split
double[] splitInfo = this.BestSplit(currNode.rows);
int colIdx = (int)splitInfo[0];
double splitVal = splitInfo[1];
if (colIdx == -1) // unable to split
{
currNode.isLeaf = true;
continue;
}
// 3. apply split properties
currNode.colIdx = colIdx; // aka split col
currNode.thresh = splitVal;
List"lt"int"gt" leftIdxs = new List"lt"int"gt"();
List"lt"int"gt" rightIdxs = new List"lt"int"gt"();
for (int k = 0; k "lt" currNode.rows.Count; ++k)
{
int r = currNode.rows[k];
if (this.trainX[r][colIdx] "lte" splitVal)
leftIdxs.Add(r);
else
rightIdxs.Add(r);
}
// 4. create and attach children
currNode.left = leftID;
this.tree[leftID] = new Node()
{
id = leftID,
rows = leftIdxs,
value = this.TreeTargetMean(leftIdxs)
};
currNode.right = rightID;
this.tree[rightID] = new Node()
{
id = rightID,
rows = rightIdxs,
value = this.TreeTargetMean(rightIdxs)
};
} // main loop
// delete row info (for ensembles)
if (this.saveRows == false)
{
for (int i = 0; i "lt" this.tree.Count; ++i)
if (this.tree[i] != null)
this.tree[i].rows = null;
}
} // Train()
// ------------------------------------------------------
public double Predict(double[] x)
{
int p = 0;
double lastValidValue = 0.0;
while (p != -1 && p "lt" this.tree.Count)
{
Node currNode = this.tree[p];
if (currNode == null) break;
lastValidValue = currNode.value;
if (currNode.isLeaf ||
currNode.colIdx == -1) break;
if (x[currNode.colIdx] "lte" currNode.thresh)
p = currNode.left;
else
p = currNode.right;
}
return lastValidValue;
}
// ------------------------------------------------------
public void Explain(double[] x)
{
int p = 0;
double lastValidValue = 0.0;
Node currNode = null;
string s = "\nIF \n";
while (p != -1 && p "lt" this.tree.Count)
{
currNode = this.tree[p];
if (currNode == null) break;
lastValidValue = currNode.value;
if (currNode.isLeaf == true) break;
s += "column " + currNode.colIdx + " ";
if (x[currNode.colIdx] "lte" currNode.thresh)
{
s += " "lte" " +
currNode.thresh.ToString("F4").PadLeft(8) +
" AND \n";
p = currNode.left;
}
else
{
s += " "gt" " +
currNode.thresh.ToString("F4").PadLeft(8) +
" AND \n";
p = currNode.right;
}
}
int nid;
if (currNode == null)
nid = -1;
else
nid = currNode.id;
s += "THEN node [" + nid + "] predicted = " +
currNode.value.ToString("F4");
Console.WriteLine(s);
}
// ------------------------------------------------------
public double Accuracy(double[][] dataX, double[] dataY,
double pctClose)
{
int numCorrect = 0; int numWrong = 0;
for (int i = 0; i "lt" dataX.Length; ++i)
{
double actualY = dataY[i];
double predY = this.Predict(dataX[i]);
if (Math.Abs(predY - actualY) "lt"
(pctClose * Math.Abs(actualY)))
++numCorrect;
else
++numWrong;
}
return (numCorrect * 1.0) / (numWrong + numCorrect);
}
// ------------------------------------------------------
public double MSE(double[][] dataX, double[] dataY)
{
// standard machine learning MSE, not tree MSE
int n = dataX.Length;
double sum = 0.0;
for (int i = 0; i "lt" n; ++i)
{
double actualY = dataY[i];
double predY = this.Predict(dataX[i]);
sum += (actualY - predY) * (actualY - predY);
}
return sum / n;
}
// ------------------------------------------------------
public void Display()
{
for (int i = 0; i "lt" this.tree.Count; ++i)
{
Node n = this.tree[i];
// check for empty nodes
if (n == null) continue;
string s1 = "ID " +
n.id.ToString().PadRight(3) + " | ";
string s2 = "sc " +
n.colIdx.ToString().PadLeft(3) + " | ";
string s3 = "sv " +
n.thresh.ToString("F4").PadLeft(8) + " | ";
string s4 = "L " +
n.left.ToString().PadLeft(3) + " | ";
string s5 = "R " +
n.right.ToString().PadLeft(3) + " | ";
string s6 = "py " +
n.value.ToString("F4").PadLeft(8) + " | ";
string s7 = "leaf " +
(n.isLeaf == true ? "T" : "F") + " | ";
string s8;
if (n.rows == null)
s8 = "rc 0";
else
s8 = "rc " + n.rows.Count;
Console.WriteLine(s1 + s2 + s3 + s4 +
s5 + s6 + s7 + s8);
}
} // Display()
// ------------------------------------------------------
private double[] BestSplit(List"lt"int"gt" rows)
{
int bestColIdx = -1;
double bestThresh = 0.0;
double bestGain = -1.0;
int nRows = rows.Count;
int nCols = this.trainX[0].Length;
int[] colIndices = new int[nCols];
for (int k = 0; k "lt" nCols; ++k)
colIndices[k] = k;
// feature sampling
for (int i = 0; i "lt" nCols; ++i)
{
int ri = rnd.Next(i, nCols);
int tmp = colIndices[i];
colIndices[i] = colIndices[ri];
colIndices[ri] = tmp;
}
int numColsToUse;
if (this.numSplitCols == -1)
numColsToUse = nCols;
else
numColsToUse = Math.Min(this.numSplitCols, nCols);
int[] sortedRows = new int[nRows];
double[] featureKeys = new double[nRows];
double totalSum = 0.0;
for (int i = 0; i "lt" nRows; ++i)
totalSum += this.trainY[rows[i]];
for (int j = 0; j "lt" numColsToUse; ++j)
{
int colIdx = colIndices[j];
for (int i = 0; i "lt" nRows; ++i)
{
int r = rows[i];
sortedRows[i] = r;
featureKeys[i] = this.trainX[r][colIdx];
}
Array.Sort(featureKeys, sortedRows);
double leftSum = 0.0;
for (int i = 0; i "lt" nRows - 1; ++i)
{
leftSum += this.trainY[sortedRows[i]];
int leftCount = i + 1;
int rightCount = nRows - leftCount;
if (featureKeys[i] == featureKeys[i + 1])
continue; // duplicate values
if (leftCount "lt" this.minLeaf ||
rightCount "lt" this.minLeaf)
continue;
double rightSum = totalSum - leftSum;
// maximizing Gain = (G_L^2 / N_L) + (G_R^2 / N_R)
// equivalent to maximizing explicit weighted
// variance reduction
double gain = (leftSum * leftSum / leftCount) +
(rightSum * rightSum / rightCount);
if (gain "gt" bestGain)
{
bestGain = gain;
bestColIdx = colIdx;
// bestThresh =
// (featureKeys[i] + featureKeys[i + 1]) / 2.0;
bestThresh = featureKeys[i] +
(featureKeys[i + 1] - featureKeys[i]) / 2.0;
}
}
}
// using a tuple would create version dependency
double[] result = new double[2];
result[0] = 1.0 * bestColIdx;
result[1] = bestThresh;
return result;
}
// ------------------------------------------------------
private double TreeTargetMean(List"lt"int"gt" rows)
{
if (rows == null || rows.Count == 0)
return 0.0;
double sum = 0.0;
for (int i = 0; i "lt" rows.Count; ++i)
sum += this.trainY[rows[i]];
return sum / rows.Count;
}
} // DecisionTreeRegressor
// ========================================================
} // ns
Training data:
# synthetic_train_200.txt # -0.1660, 0.4406, -0.9998, -0.3953, -0.7065, 0.4840 0.0776, -0.1616, 0.3704, -0.5911, 0.7562, 0.1568 -0.9452, 0.3409, -0.1654, 0.1174, -0.7192, 0.8054 0.9365, -0.3732, 0.3846, 0.7528, 0.7892, 0.1345 -0.8299, -0.9219, -0.6603, 0.7563, -0.8033, 0.7955 0.0663, 0.3838, -0.3690, 0.3730, 0.6693, 0.3206 -0.9634, 0.5003, 0.9777, 0.4963, -0.4391, 0.7377 -0.1042, 0.8172, -0.4128, -0.4244, -0.7399, 0.4801 -0.9613, 0.3577, -0.5767, -0.4689, -0.0169, 0.6861 -0.7065, 0.1786, 0.3995, -0.7953, -0.1719, 0.5569 0.3888, -0.1716, -0.9001, 0.0718, 0.3276, 0.2500 0.1731, 0.8068, -0.7251, -0.7214, 0.6148, 0.3297 -0.2046, -0.6693, 0.8550, -0.3045, 0.5016, 0.2129 0.2473, 0.5019, -0.3022, -0.4601, 0.7918, 0.2613 -0.1438, 0.9297, 0.3269, 0.2434, -0.7705, 0.5171 0.1568, -0.1837, -0.5259, 0.8068, 0.1474, 0.3307 -0.9943, 0.2343, -0.3467, 0.0541, 0.7719, 0.5581 0.2467, -0.9684, 0.8589, 0.3818, 0.9946, 0.1092 -0.6553, -0.7257, 0.8652, 0.3936, -0.8680, 0.7018 0.8460, 0.4230, -0.7515, -0.9602, -0.9476, 0.1996 -0.9434, -0.5076, 0.7201, 0.0777, 0.1056, 0.5664 0.9392, 0.1221, -0.9627, 0.6013, -0.5341, 0.1533 0.6142, -0.2243, 0.7271, 0.4942, 0.1125, 0.1661 0.4260, 0.1194, -0.9749, -0.8561, 0.9346, 0.2230 0.1362, -0.5934, -0.4953, 0.4877, -0.6091, 0.3810 0.6937, -0.5203, -0.0125, 0.2399, 0.6580, 0.1460 -0.6864, -0.9628, -0.8600, -0.0273, 0.2127, 0.5387 0.9772, 0.1595, -0.2397, 0.1019, 0.4907, 0.1611 0.3385, -0.4702, -0.8673, -0.2598, 0.2594, 0.2270 -0.8669, -0.4794, 0.6095, -0.6131, 0.2789, 0.4700 0.0493, 0.8496, -0.4734, -0.8681, 0.4701, 0.3516 0.8639, -0.9721, -0.5313, 0.2336, 0.8980, 0.1412 0.9004, 0.1133, 0.8312, 0.2831, -0.2200, 0.1782 0.0991, 0.8524, 0.8375, -0.2102, 0.9265, 0.2150 -0.6521, -0.7473, -0.7298, 0.0113, -0.9570, 0.7422 0.6190, -0.3105, 0.8802, 0.1640, 0.7577, 0.1056 0.6895, 0.8108, -0.0802, 0.0927, 0.5972, 0.2214 0.1982, -0.9689, 0.1870, -0.1326, 0.6147, 0.1310 -0.3695, 0.7858, 0.1557, -0.6320, 0.5759, 0.3773 -0.1596, 0.3581, 0.8372, -0.9992, 0.9535, 0.2071 -0.2468, 0.9476, 0.2094, 0.6577, 0.1494, 0.4132 0.1737, 0.5000, 0.7166, 0.5102, 0.3961, 0.2611 0.7290, -0.3546, 0.3416, -0.0983, -0.2358, 0.1332 -0.3652, 0.2438, -0.1395, 0.9476, 0.3556, 0.4170 -0.6029, -0.1466, -0.3133, 0.5953, 0.7600, 0.4334 -0.4596, -0.4953, 0.7098, 0.0554, 0.6043, 0.2775 0.1450, 0.4663, 0.0380, 0.5418, 0.1377, 0.2931 -0.8636, -0.2442, -0.8407, 0.9656, -0.6368, 0.7429 0.6237, 0.7499, 0.3768, 0.1390, -0.6781, 0.2185 -0.5499, 0.1850, -0.3755, 0.8326, 0.8193, 0.4399 -0.4858, -0.7782, -0.6141, -0.0008, 0.4572, 0.4197 0.7033, -0.1683, 0.2334, -0.5327, -0.7961, 0.1776 0.0317, -0.0457, -0.6947, 0.2436, 0.0880, 0.3345 0.5031, -0.5559, 0.0387, 0.5706, -0.9553, 0.3107 -0.3513, 0.7458, 0.6894, 0.0769, 0.7332, 0.3170 0.2205, 0.5992, -0.9309, 0.5405, 0.4635, 0.3532 -0.4806, -0.4859, 0.2646, -0.3094, 0.5932, 0.3202 0.9809, -0.3995, -0.7140, 0.8026, 0.0831, 0.1600 0.9495, 0.2732, 0.9878, 0.0921, 0.0529, 0.1289 -0.9476, -0.6792, 0.4913, -0.9392, -0.2669, 0.5966 0.7247, 0.3854, 0.3819, -0.6227, -0.1162, 0.1550 -0.5922, -0.5045, -0.4757, 0.5003, -0.0860, 0.5863 -0.8861, 0.0170, -0.5761, 0.5972, -0.4053, 0.7301 0.6877, -0.2380, 0.4997, 0.0223, 0.0819, 0.1404 0.9189, 0.6079, -0.9354, 0.4188, -0.0700, 0.1907 -0.1428, -0.7820, 0.2676, 0.6059, 0.3936, 0.2790 0.5324, -0.3151, 0.6917, -0.1425, 0.6480, 0.1071 -0.8432, -0.9633, -0.8666, -0.0828, -0.7733, 0.7784 -0.9444, 0.5097, -0.2103, 0.4939, -0.0952, 0.6787 -0.0520, 0.6063, -0.1952, 0.8094, -0.9259, 0.4836 0.5477, -0.7487, 0.2370, -0.9793, 0.0773, 0.1241 0.2450, 0.8116, 0.9799, 0.4222, 0.4636, 0.2355 0.8186, -0.1983, -0.5003, -0.6531, -0.7611, 0.1511 -0.4714, 0.6382, -0.3788, 0.9648, -0.4667, 0.5950 0.0673, -0.3711, 0.8215, -0.2669, -0.1328, 0.2677 -0.9381, 0.4338, 0.7820, -0.9454, 0.0441, 0.5518 -0.3480, 0.7190, 0.1170, 0.3805, -0.0943, 0.4724 -0.9813, 0.1535, -0.3771, 0.0345, 0.8328, 0.5438 -0.1471, -0.5052, -0.2574, 0.8637, 0.8737, 0.3042 -0.5454, -0.3712, -0.6505, 0.2142, -0.1728, 0.5783 0.6327, -0.6297, 0.4038, -0.5193, 0.1484, 0.1153 -0.5424, 0.3282, -0.0055, 0.0380, -0.6506, 0.6613 0.1414, 0.9935, 0.6337, 0.1887, 0.9520, 0.2540 -0.9351, -0.8128, -0.8693, -0.0965, -0.2491, 0.7353 0.9507, -0.6640, 0.9456, 0.5349, 0.6485, 0.1059 -0.0462, -0.9737, -0.2940, -0.0159, 0.4602, 0.2606 -0.0627, -0.0852, -0.7247, -0.9782, 0.5166, 0.2977 0.0478, 0.5098, -0.0723, -0.7504, -0.3750, 0.3335 0.0090, 0.3477, 0.5403, -0.7393, -0.9542, 0.4415 -0.9748, 0.3449, 0.3736, -0.1015, 0.8296, 0.4358 0.2887, -0.9895, -0.0311, 0.7186, 0.6608, 0.2057 0.1570, -0.4518, 0.1211, 0.3435, -0.2951, 0.3244 0.7117, -0.6099, 0.4946, -0.4208, 0.5476, 0.1096 -0.2929, -0.5726, 0.5346, -0.3827, 0.4665, 0.2465 0.4889, -0.5572, -0.5718, -0.6021, -0.7150, 0.2163 -0.7782, 0.3491, 0.5996, -0.8389, -0.5366, 0.6516 -0.5847, 0.8347, 0.4226, 0.1078, -0.3910, 0.6134 0.8469, 0.4121, -0.0439, -0.7476, 0.9521, 0.1571 -0.6803, -0.5948, -0.1376, -0.1916, -0.7065, 0.7156 0.2878, 0.5086, -0.5785, 0.2019, 0.4979, 0.2980 0.2764, 0.1943, -0.4090, 0.4632, 0.8906, 0.2960 -0.8877, 0.6705, -0.6155, -0.2098, -0.3998, 0.7107 -0.8398, 0.8093, -0.2597, 0.0614, -0.0118, 0.6502 -0.8476, 0.0158, -0.4769, -0.2859, -0.7839, 0.7715 0.5751, -0.7868, 0.9714, -0.6457, 0.1448, 0.1175 0.4802, -0.7001, 0.1022, -0.5668, 0.5184, 0.1090 0.4458, -0.6469, 0.7239, -0.9604, 0.7205, 0.0779 0.5175, 0.4339, 0.9747, -0.4438, -0.9924, 0.2879 0.8678, 0.7158, 0.4577, 0.0334, 0.4139, 0.1678 0.5406, 0.5012, 0.2264, -0.1963, 0.3946, 0.2088 -0.9938, 0.5498, 0.7928, -0.5214, -0.7585, 0.7687 0.7661, 0.0863, -0.4266, -0.7233, -0.4197, 0.1466 0.2277, -0.3517, -0.0853, -0.1118, 0.6563, 0.1767 0.3499, -0.5570, -0.0655, -0.3705, 0.2537, 0.1632 0.7547, -0.1046, 0.5689, -0.0861, 0.3125, 0.1257 0.8186, 0.2110, 0.5335, 0.0094, -0.0039, 0.1391 0.6858, -0.8644, 0.1465, 0.8855, 0.0357, 0.1845 -0.4967, 0.4015, 0.0805, 0.8977, 0.2487, 0.4663 0.6760, -0.9841, 0.9787, -0.8446, -0.3557, 0.1509 -0.1203, -0.4885, 0.6054, -0.0443, -0.7313, 0.4854 0.8557, 0.7919, -0.0169, 0.7134, -0.1628, 0.2002 0.0115, -0.6209, 0.9300, -0.4116, -0.7931, 0.4052 -0.7114, -0.9718, 0.4319, 0.1290, 0.5892, 0.3661 0.3915, 0.5557, -0.1870, 0.2955, -0.6404, 0.2954 -0.3564, -0.6548, -0.1827, -0.5172, -0.1862, 0.4622 0.2392, -0.4959, 0.5857, -0.1341, -0.2850, 0.2470 -0.3394, 0.3947, -0.4627, 0.6166, -0.4094, 0.5325 0.7107, 0.7768, -0.6312, 0.1707, 0.7964, 0.2757 -0.1078, 0.8437, -0.4420, 0.2177, 0.3649, 0.4028 -0.3139, 0.5595, -0.6505, -0.3161, -0.7108, 0.5546 0.4335, 0.3986, 0.3770, -0.4932, 0.3847, 0.1810 -0.2562, -0.2894, -0.8847, 0.2633, 0.4146, 0.4036 0.2272, 0.2966, -0.6601, -0.7011, 0.0284, 0.2778 -0.0743, -0.1421, -0.0054, -0.6770, -0.3151, 0.3597 -0.4762, 0.6891, 0.6007, -0.1467, 0.2140, 0.4266 -0.4061, 0.7193, 0.3432, 0.2669, -0.7505, 0.6147 -0.0588, 0.9731, 0.8966, 0.2902, -0.6966, 0.4955 -0.0627, -0.1439, 0.1985, 0.6999, 0.5022, 0.3077 0.1587, 0.8494, -0.8705, 0.9827, -0.8940, 0.4263 -0.7850, 0.2473, -0.9040, -0.4308, -0.8779, 0.7199 0.4070, 0.3369, -0.2428, -0.6236, 0.4940, 0.2215 -0.0242, 0.0513, -0.9430, 0.2885, -0.2987, 0.3947 -0.5416, -0.1322, -0.2351, -0.0604, 0.9590, 0.3683 0.1055, 0.7783, -0.2901, -0.5090, 0.8220, 0.2984 -0.9129, 0.9015, 0.1128, -0.2473, 0.9901, 0.4776 -0.9378, 0.1424, -0.6391, 0.2619, 0.9618, 0.5368 0.7498, -0.0963, 0.4169, 0.5549, -0.0103, 0.1614 -0.2612, -0.7156, 0.4538, -0.0460, -0.1022, 0.3717 0.7720, 0.0552, -0.1818, -0.4622, -0.8560, 0.1685 -0.4177, 0.0070, 0.9319, -0.7812, 0.3461, 0.3052 -0.0001, 0.5542, -0.7128, -0.8336, -0.2016, 0.3803 0.5356, -0.4194, -0.5662, -0.9666, -0.2027, 0.1776 -0.2378, 0.3187, -0.8582, -0.6948, -0.9668, 0.5474 -0.1947, -0.3579, 0.1158, 0.9869, 0.6690, 0.2992 0.3992, 0.8365, -0.9205, -0.8593, -0.0520, 0.3154 -0.0209, 0.0793, 0.7905, -0.1067, 0.7541, 0.1864 -0.4928, -0.4524, -0.3433, 0.0951, -0.5597, 0.6261 -0.8118, 0.7404, -0.5263, -0.2280, 0.1431, 0.6349 0.0516, -0.8480, 0.7483, 0.9023, 0.6250, 0.1959 -0.3212, 0.1093, 0.9488, -0.3766, 0.3376, 0.2735 -0.3481, 0.5490, -0.3484, 0.7797, 0.5034, 0.4379 -0.5785, -0.9170, -0.3563, -0.9258, 0.3877, 0.4121 0.3407, -0.1391, 0.5356, 0.0720, -0.9203, 0.3458 -0.3287, -0.8954, 0.2102, 0.0241, 0.2349, 0.3247 -0.1353, 0.6954, -0.0919, -0.9692, 0.7461, 0.3338 0.9036, -0.8982, -0.5299, -0.8733, -0.1567, 0.1187 0.7277, -0.8368, -0.0538, -0.7489, 0.5458, 0.0830 0.9049, 0.8878, 0.2279, 0.9470, -0.3103, 0.2194 0.7957, -0.1308, -0.5284, 0.8817, 0.3684, 0.2172 0.4647, -0.4931, 0.2010, 0.6292, -0.8918, 0.3371 -0.7390, 0.6849, 0.2367, 0.0626, -0.5034, 0.7039 -0.1567, -0.8711, 0.7940, -0.5932, 0.6525, 0.1710 0.7635, -0.0265, 0.1969, 0.0545, 0.2496, 0.1445 0.7675, 0.1354, -0.7698, -0.5460, 0.1920, 0.1728 -0.5211, -0.7372, -0.6763, 0.6897, 0.2044, 0.5217 0.1913, 0.1980, 0.2314, -0.8816, 0.5006, 0.1998 0.8964, 0.0694, -0.6149, 0.5059, -0.9854, 0.1825 0.1767, 0.7104, 0.2093, 0.6452, 0.7590, 0.2832 -0.3580, -0.7541, 0.4426, -0.1193, -0.7465, 0.5657 -0.5996, 0.5766, -0.9758, -0.3933, -0.9572, 0.6800 0.9950, 0.1641, -0.4132, 0.8579, 0.0142, 0.2003 -0.4717, -0.3894, -0.2567, -0.5111, 0.1691, 0.4266 0.3917, -0.8561, 0.9422, 0.5061, 0.6123, 0.1212 -0.0366, -0.1087, 0.3449, -0.1025, 0.4086, 0.2475 0.3633, 0.3943, 0.2372, -0.6980, 0.5216, 0.1925 -0.5325, -0.6466, -0.2178, -0.3589, 0.6310, 0.3568 0.2271, 0.5200, -0.1447, -0.8011, -0.7699, 0.3128 0.6415, 0.1993, 0.3777, -0.0178, -0.8237, 0.2181 -0.5298, -0.0768, -0.6028, -0.9490, 0.4588, 0.4356 0.6870, -0.1431, 0.7294, 0.3141, 0.1621, 0.1632 -0.5985, 0.0591, 0.7889, -0.3900, 0.7419, 0.2945 0.3661, 0.7984, -0.8486, 0.7572, -0.6183, 0.3449 0.6995, 0.3342, -0.3113, -0.6972, 0.2707, 0.1712 0.2565, 0.9126, 0.1798, -0.6043, -0.1413, 0.2893 -0.3265, 0.9839, -0.2395, 0.9854, 0.0376, 0.4770 0.2690, -0.1722, 0.9818, 0.8599, -0.7015, 0.3954 -0.2102, -0.0768, 0.1219, 0.5607, -0.0256, 0.3949 0.8216, -0.9555, 0.6422, -0.6231, 0.3715, 0.0801 -0.2896, 0.9484, -0.7545, -0.6249, 0.7789, 0.4370 -0.9985, -0.5448, -0.7092, -0.5931, 0.7926, 0.5402
Test data:
# synthetic_test_40.txt # 0.7462, 0.4006, -0.0590, 0.6543, -0.0083, 0.1935 0.8495, -0.2260, -0.0142, -0.4911, 0.7699, 0.1078 -0.2335, -0.4049, 0.4352, -0.6183, -0.7636, 0.5088 0.1810, -0.5142, 0.2465, 0.2767, -0.3449, 0.3136 -0.8650, 0.7611, -0.0801, 0.5277, -0.4922, 0.7140 -0.2358, -0.7466, -0.5115, -0.8413, -0.3943, 0.4533 0.4834, 0.2300, 0.3448, -0.9832, 0.3568, 0.1360 -0.6502, -0.6300, 0.6885, 0.9652, 0.8275, 0.3046 -0.3053, 0.5604, 0.0929, 0.6329, -0.0325, 0.4756 -0.7995, 0.0740, -0.2680, 0.2086, 0.9176, 0.4565 -0.2144, -0.2141, 0.5813, 0.2902, -0.2122, 0.4119 -0.7278, -0.0987, -0.3312, -0.5641, 0.8515, 0.4438 0.3793, 0.1976, 0.4933, 0.0839, 0.4011, 0.1905 -0.8568, 0.9573, -0.5272, 0.3212, -0.8207, 0.7415 -0.5785, 0.0056, -0.7901, -0.2223, 0.0760, 0.5551 0.0735, -0.2188, 0.3925, 0.3570, 0.3746, 0.2191 0.1230, -0.2838, 0.2262, 0.8715, 0.1938, 0.2878 0.4792, -0.9248, 0.5295, 0.0366, -0.9894, 0.3149 -0.4456, 0.0697, 0.5359, -0.8938, 0.0981, 0.3879 0.8629, -0.8505, -0.4464, 0.8385, 0.5300, 0.1769 0.1995, 0.6659, 0.7921, 0.9454, 0.9970, 0.2330 -0.0249, -0.3066, -0.2927, -0.4923, 0.8220, 0.2437 0.4513, -0.9481, -0.0770, -0.4374, -0.9421, 0.2879 -0.3405, 0.5931, -0.3507, -0.3842, 0.8562, 0.3987 0.9538, 0.0471, 0.9039, 0.7760, 0.0361, 0.1706 -0.0887, 0.2104, 0.9808, 0.5478, -0.3314, 0.4128 -0.8220, -0.6302, 0.0537, -0.1658, 0.6013, 0.4306 -0.4123, -0.2880, 0.9074, -0.0461, -0.4435, 0.5144 0.0060, 0.2867, -0.7775, 0.5161, 0.7039, 0.3599 -0.7968, -0.5484, 0.9426, -0.4308, 0.8148, 0.2979 0.7811, 0.8450, -0.6877, 0.7594, 0.2640, 0.2362 -0.6802, -0.1113, -0.8325, -0.6694, -0.6056, 0.6544 0.3821, 0.1476, 0.7466, -0.5107, 0.2592, 0.1648 0.7265, 0.9683, -0.9803, -0.4943, -0.5523, 0.2454 -0.9049, -0.9797, -0.0196, -0.9090, -0.4433, 0.6447 -0.4607, 0.1811, -0.2389, 0.4050, -0.0078, 0.5229 0.2664, -0.2932, -0.4259, -0.7336, 0.8742, 0.1834 -0.4507, 0.1029, -0.6294, -0.1158, -0.6294, 0.6081 0.8948, -0.0124, 0.9278, 0.2899, -0.0314, 0.1534 -0.1323, -0.8813, -0.0146, -0.0697, 0.6135, 0.2386
.NET Test Automation Recipes
Software Testing
SciPy Programming Succinctly
Keras Succinctly
R Programming
Visual Studio Live
Microsoft MLADS Conference
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Machine Learning Week
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