Documentation
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Index ¶
- func LoadExamScore() (X, Y *mat.Dense)
- func LoadMicroChipTest() (X, Y *mat.Dense)
- func LoadMnist() (X, Y *mat.Dense)
- func LoadMnistWeights() (Theta1, Theta2 *mat.Dense)
- func LoadOctaveBin(filename string) map[string]*mat.Dense
- func MakeRegression(kwargs map[string]interface{}) (X, y, Coef *mat.Dense)
- type MLDataset
Examples ¶
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func LoadExamScore ¶
LoadExamScore loads data from ex2data1 from Andrew Ng machine learning course
Example ¶
X, Y := LoadExamScore()
xr, xc := X.Dims()
yr, yc := Y.Dims()
fmt.Printf("X %d,%d Y %d,%d\n", xr, xc, yr, yc)
fmt.Println(matstr(X.Slice(0, 3, 0, 2), Y.Slice(0, 3, 0, 1)))
Output: X 100,2 Y 100,1 34.62365962451697 78.0246928153624 0 30.28671076822607 43.89499752400101 0 35.84740876993872 72.90219802708364 0
func LoadMicroChipTest ¶
LoadMicroChipTest loads data from ex2data2 from Andrew Ng machine learning course
Example ¶
X, Y := LoadMicroChipTest()
xr, xc := X.Dims()
yr, yc := Y.Dims()
fmt.Printf("X %d,%d Y %d,%d\n", xr, xc, yr, yc)
fmt.Println(matstr(X.Slice(0, 3, 0, 2), Y.Slice(0, 3, 0, 1)))
Output: X 118,2 Y 118,1 0.051267 0.69956 1 -0.092742 0.68494 1 -0.21371 0.69225 1
func LoadMnist ¶
LoadMnist loads mnist data 5000x400,5000x1
Example ¶
X, Y := LoadMnist()
xr, xc := X.Dims()
yr, yc := Y.Dims()
fmt.Printf("X %d,%d Y %d,%d\n", xr, xc, yr, yc)
fmt.Printf("%.6f %.6f\n", mat.Sum(X), mat.Sum(Y))
Output: X 5000,400 Y 5000,1 262678.260160 27500.000000
func LoadMnistWeights ¶
LoadMnistWeights loads mnist weights
Example ¶
Theta1, Theta2 := LoadMnistWeights()
xr, xc := Theta1.Dims()
yr, yc := Theta2.Dims()
fmt.Printf("Theta1 %d,%d Theta2 %d,%d\n", xr, xc, yr, yc)
fmt.Printf("%.6f %.6f\n", mat.Sum(Theta1), mat.Sum(Theta2))
Output: Theta1 25,401 Theta2 10,26 9.242644 -100.083444
func LoadOctaveBin ¶
LoadOctaveBin reads an (possibly gzipped) octave binary file into a map of *map.Dense
func MakeRegression ¶
MakeRegression Generate a random regression problem n_samples : int, optional (default=100) The number of samples. n_features : int, optional (default=100) The number of features. n_informative : int, optional (default=10) The number of informative features, i.e., the number of features used to build the linear model used to generate the output. n_targets : int, optional (default=1) The number of regression targets, i.e., the dimension of the y output vector associated with a sample. By default, the output is a scalar. bias : float64 or []float64 or mat.Matrix, optional (default=0.0) The bias term in the underlying linear model. effective_rank : int , optional (default=None) currently unused tail_strength : float between 0.0 and 1.0, optional (default=0.5) currently unused shuffle : boolean, optional (default=True) coef : boolean. the coefficients of the underlying linear model are returned regardless its value. random_state : *math.Rand optional (default=nil)
Example ¶
X, Y, _ := MakeRegression(map[string]interface{}{"n_samples": 200, "n_features": 3, "n_informative": 2, "n_targets": 2,
"bias": []float64{1., 2.},
"shuffle": true,
})
xr, xc := X.Dims()
fmt.Println("X", xr, xc)
yr, yc := Y.Dims()
fmt.Println("Y", yr, yc)
Output: X 200 3 Y 200 2
Types ¶
type MLDataset ¶
type MLDataset struct {
Data [][]float64 `json:"data,omitempty"`
Target []float64 `json:"target,omitempty"`
TargetNames []string `json:"target_names,omitempty"`
DESCR string `json:"DESCR,omitempty"`
FeatureNames []string `json:"feature_names,omitempty"`
X, Y *mat.Dense
}
MLDataset structure returned by LoadIris,LoadBreastCancer,LoadDiabetes,LoadBoston
func LoadBoston ¶
func LoadBoston() (ds *MLDataset)
LoadBoston load the boston housing dataset
Example ¶
X, Y := LoadBoston().GetXY()
xr, xc := X.Dims()
yr, yc := Y.Dims()
fmt.Printf("X %d,%d Y %d,%d\n", xr, xc, yr, yc)
fmt.Println(matstr(X.Slice(0, 3, 0, 4), Y.Slice(0, 3, 0, 1)))
Output: X 506,13 Y 506,1 0.00632 18 2.31 0 24 0.02731 0 7.07 0 21.6 0.02729 0 7.07 0 34.7
func LoadBreastCancer ¶
func LoadBreastCancer() (ds *MLDataset)
LoadBreastCancer load the breat cancer dataset
Example ¶
ds := LoadBreastCancer()
X, Y := ds.X, ds.Y
xr, xc := X.Dims()
yr, yc := Y.Dims()
fmt.Printf("X %d,%d Y %d,%d\n", xr, xc, yr, yc)
fmt.Println(matstr(X.Slice(0, 3, 0, 4), Y.Slice(0, 3, 0, 1)))
Output: X 569,30 Y 569,1 17.99 10.38 122.8 1001 0 20.57 17.77 132.9 1326 0 19.69 21.25 130 1203 0
func LoadDiabetes ¶
func LoadDiabetes() (ds *MLDataset)
LoadDiabetes load the diabetes dataset
Example ¶
ds := LoadDiabetes()
X, Y := ds.X, ds.Y
xr, xc := X.Dims()
yr, yc := Y.Dims()
fmt.Printf("X %d,%d Y %d,%d\n", xr, xc, yr, yc)
fmt.Println(matstr(X.Slice(0, 3, 0, 4), Y.Slice(0, 3, 0, 1)))
Output: X 442,10 Y 442,1 0.0380759064334241 0.0506801187398187 0.0616962065186885 0.0218723549949558 151 -0.00188201652779104 -0.044641636506989 -0.0514740612388061 -0.0263278347173518 75 0.0852989062966783 0.0506801187398187 0.0444512133365941 -0.00567061055493425 141
func LoadIris ¶
func LoadIris() (ds *MLDataset)
LoadIris load the iris dataset
Example ¶
ds := LoadIris()
X, Y := ds.X, ds.Y
xr, xc := X.Dims()
yr, yc := Y.Dims()
fmt.Printf("X %d,%d Y %d,%d\n", xr, xc, yr, yc)
fmt.Println(matstr(X.Slice(0, 3, 0, 4), Y.Slice(0, 3, 0, 1)))
Output: X 150,4 Y 150,1 5.1 3.5 1.4 0.2 0 4.9 3 1.4 0.2 0 4.7 3.2 1.3 0.2 0
Source Files