/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair.</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public MultipleLinearRegression Learn(double[][] x, double[] y, double[] weights = null)
{
//var Xt = x.Transpose();
if (UseIntercept)
{
x = x.InsertColumn(value: 1.0);
}
decomposition = x.Decompose(leastSquares: IsRobust);
double[] coefficients = decomposition.Solve(y);
if (UseIntercept)
{
return(new MultipleLinearRegression()
{
Weights = coefficients.Get(0, -1),
Intercept = coefficients.Get(-1)
});
}
else
{
return(new MultipleLinearRegression()
{
Weights = coefficients,
});
}
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair.</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public SimpleLinearRegression Learn(double[] x, double[] y, double[] weights = null)
{
double[][] X = Jagged.ColumnVector(x);
if (UseIntercept)
{
X = X.InsertColumn(value: 1);
}
decomposition = X.Decompose(leastSquares: IsRobust);
double[] coefficients = decomposition.Solve(y);
if (UseIntercept)
{
return(new SimpleLinearRegression()
{
Slope = coefficients[0],
Intercept = coefficients[1]
});
}
else
{
return(new SimpleLinearRegression()
{
Slope = coefficients[0],
});
}
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public MultivariateLinearRegression Learn(double[][] x, double[][] y, double[] weights = null)
{
if (UseIntercept)
{
x = x.InsertColumn(value: 1.0);
}
if (weights != null)
{
double[] sqrtW = weights.Sqrt();
x = Elementwise.Multiply(x, sqrtW, dimension: 1);
y = Elementwise.Multiply(y, sqrtW, dimension: 1);
}
decomposition = x.Decompose(leastSquares: IsRobust);
double[][] coefficients = decomposition.Solve(y);
if (UseIntercept)
{
return(new MultivariateLinearRegression()
{
Weights = coefficients.Get(0, -1, null),
Intercepts = coefficients.GetRow(-1)
});
}
return(new MultivariateLinearRegression()
{
Weights = coefficients,
});
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public SimpleLinearRegression Learn(double[] x, double[] y, double[] weights = null)
{
double[][] X = Jagged.ColumnVector(x);
if (UseIntercept)
{
X = X.InsertColumn(value: 1);
}
if (weights != null)
{
double[] sqrtW = weights.Sqrt();
X = Elementwise.Multiply(X, sqrtW, dimension: 1, result: X);
y = Elementwise.Multiply(y, sqrtW);
}
decomposition = X.Decompose(leastSquares: IsRobust);
double[] coefficients = decomposition.Solve(y);
if (UseIntercept)
{
return(new SimpleLinearRegression()
{
Slope = coefficients[0],
Intercept = coefficients[1]
});
}
else
{
return(new SimpleLinearRegression()
{
Slope = coefficients[0],
});
}
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public MultivariateLinearRegression Learn(double[][] x, double[][] y, double[] weights = null)
{
if (weights != null)
{
throw new ArgumentException(Accord.Properties.Resources.NotSupportedWeights, "weights");
}
if (UseIntercept)
{
x = x.InsertColumn(value: 1.0);
}
decomposition = x.Decompose(leastSquares: IsRobust);
double[][] coefficients = decomposition.Solve(y);
if (UseIntercept)
{
return(new MultivariateLinearRegression()
{
Weights = coefficients.Get(0, -1, null),
Intercepts = coefficients.GetRow(-1)
});
}
return(new MultivariateLinearRegression()
{
Weights = coefficients,
});
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair (if supported by the learning algorithm).</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public SimpleLinearRegression Learn(double[] x, double[] y, double[] weights = null)
{
if (weights != null)
{
throw new ArgumentException(Accord.Properties.Resources.NotSupportedWeights, "weights");
}
double[][] X = Jagged.ColumnVector(x);
if (UseIntercept)
{
X = X.InsertColumn(value: 1);
}
decomposition = X.Decompose(leastSquares: IsRobust);
double[] coefficients = decomposition.Solve(y);
if (UseIntercept)
{
return(new SimpleLinearRegression()
{
Slope = coefficients[0],
Intercept = coefficients[1]
});
}
else
{
return(new SimpleLinearRegression()
{
Slope = coefficients[0],
});
}
}
/// <summary>
/// Learns a model that can map the given inputs to the given outputs.
/// </summary>
/// <param name="x">The model inputs.</param>
/// <param name="y">The desired outputs associated with each <paramref name="x">inputs</paramref>.</param>
/// <param name="weights">The weight of importance for each input-output pair.</param>
/// <returns>
/// A model that has learned how to produce <paramref name="y" /> given <paramref name="x" />.
/// </returns>
public MultivariateLinearRegression Learn(double[][] x, double[][] y, double[] weights = null)
{
if (UseIntercept)
{
x = x.InsertColumn(value: 1.0);
}
decomposition = x.Decompose(leastSquares: IsRobust);
double[][] coefficients = decomposition.Solve(y);
if (UseIntercept)
{
return(new MultivariateLinearRegression()
{
Weights = coefficients.Get(0, -1, null),
Intercepts = coefficients.GetRow(-1)
});
}
return(new MultivariateLinearRegression()
{
Weights = coefficients,
});
}
