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Index ¶
Examples ¶
Constants ¶
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Variables ¶
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Functions ¶
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Types ¶
type Perceptron ¶
type Perceptron[T constraints.Float] struct { // contains filtered or unexported fields }
Perceptron represents a simple linear classifier for numeric data
func NewPerceptron ¶
func NewPerceptron[T constraints.Float](features [][]T, labels []T) (*Perceptron[T], error)
NewPerceptron creates a new Perceptron instance with given features and labels. It initializes weights to 1 and bias to 0.
func (*Perceptron[T]) Predict ¶
func (p *Perceptron[T]) Predict(feature []T) (T, error)
Predict returns class of the feature or error if any
func (*Perceptron[T]) Train ¶
func (p *Perceptron[T]) Train(learningRate T, epoch int, withHistory bool) ([]T, T, T, error)
Train trains the Perceptron using the given learning rate and number of epochs. If withHistory is true, it saves weights, bias, and mean error after each update for later analysis.
Example (Three_dimensional_case) ¶
// Define the training features (input vectors) in 3D space
features := [][]float64{
// Class 0 (label 0)
{1, 0, 0}, {0, 2, 0}, {1, 1, 0}, {1, 2, 0}, {0, 0, 0},
{0, 1, 1}, {2, 0, 0}, {2, 1, 1}, {3, 0, 1}, {0, 3, 0},
{1.5, 1.5, 0.5}, {0.5, 1, 0.5}, {1, 0.5, 0.2}, {0.2, 1.8, 0.3}, {1.2, 1.3, 0.4},
{2.5, 0.5, 0.2}, {0.3, 2.3, 0.1}, {1.1, 0.9, 0.3}, {1.8, 0.2, 0.2}, {0.7, 1.1, 0.4},
// Class 1 {label 1}
{1, 3, 2}, {2, 2, 2}, {2, 3, 2}, {3, 2, 2}, {2, 4, 3},
{3, 3, 2}, {4, 2, 3}, {4, 3, 3}, {3, 4, 3}, {5, 3, 3},
{3.5, 2.5, 2.5}, {2.5, 3.5, 2.5}, {4.2, 2.8, 2.7}, {3.1, 3.9, 3.0}, {2.2, 4.1, 2.8},
{3.3, 2.7, 2.6}, {4.4, 3.6, 3.2}, {2.6, 3.2, 2.4}, {3.9, 2.1, 2.3}, {3.7, 3.3, 2.9},
}
// Define the corresponding labels for each feature vector
labels := []float64{
// Class 0
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
// Class 1
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
}
// Initialize a new perceptron with the training data
p, err := NewPerceptron[float64](features, labels)
if err != nil {
log.Fatalf("failed to create perceptron: %v", err)
}
// Train the perceptron
// Parameters:
// - learning rate: 0.01
// - epochs: 300
// - verbose: true (prints progress during training)
weights, bias, meanError, err := p.Train(0.01, 300, true)
if err != nil {
log.Fatalf("failed to train perceptron: %v", err)
}
if bias < -1 && bias > 1 {
log.Println("bias seems to have a wrong value")
}
if (weights[0] < -0.3 && weights[0] > 0.3) ||
(weights[1] < -0.3 && weights[1] > 0.3) ||
(weights[2] < -0.3 && weights[2] > 0.3) {
log.Println("weights seems to have a wrong value")
}
fmt.Printf("Mean error: %.6f\n", meanError)
Output: Mean error: 0.000000
Example (Two_dimensional_case) ¶
// Define the training features (input vectors)
features := [][]float64{
// Class 0 (label 0)
{1, 0}, {0, 2}, {1, 1}, {1, 2}, {0, 0},
{0, 1}, {2, 0}, {2, 1}, {3, 0}, {0, 3},
{1.5, 1.5}, {0.5, 1}, {1, 0.5}, {0.2, 1.8}, {1.2, 1.3},
{2.5, 0.5}, {0.3, 2.3}, {1.1, 0.9}, {1.8, 0.2}, {0.7, 1.1},
// Class 1 (label 1)
{1, 3}, {2, 2}, {2, 3}, {3, 2}, {2, 4},
{3, 3}, {4, 2}, {4, 3}, {3, 4}, {5, 3},
{3.5, 2.5}, {2.5, 3.5}, {4.2, 2.8}, {3.1, 3.9}, {2.2, 4.1},
{3.3, 2.7}, {4.4, 3.6}, {2.6, 3.2}, {3.9, 2.1}, {3.7, 3.3},
}
// Define the corresponding labels for each feature vector
labels := []float64{
// Class 0
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
0, 0, 0, 0, 0,
// Class 1
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
1, 1, 1, 1, 1,
}
// Initialize a new perceptron with the training data
p, err := NewPerceptron[float64](features, labels)
if err != nil {
log.Fatalf("failed to create perceptron: %v", err)
}
// Train the perceptron
// Parameters:
// - learning rate: 0.01
// - epochs: 250
// - verbose: true (prints progress during training)
weights, bias, meanError, err := p.Train(0.01, 250, true)
if err != nil {
log.Fatalf("failed to train perceptron: %v", err)
}
if bias < -1 && bias > 1 {
log.Println("bias seems to have a wrong value")
}
if (weights[0] < -0.3 && weights[0] > 0.3) || (weights[1] < -0.3 && weights[1] > 0.3) {
log.Println("weights seems to have a wrong value")
}
fmt.Printf("Mean error: %.6f\n", meanError)
Output: Mean error: 0.000000
func (*Perceptron[T]) TrainingHistory ¶
func (p *Perceptron[T]) TrainingHistory() [][]T
TrainingHistory returns trainingHistory
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