func (dataset *inMemoryDataset) AddRowFromStrings(strings []string) error {
actualLength := len(strings)
expectedLength := dataset.numColumns
if actualLength != expectedLength {
return newRowLengthMismatchError(actualLength, expectedLength)
}
rawValues := make([]float64, actualLength)
for i, s := range strings {
value, err := dataset.columnTypes[i].PersistRawFromString(s)
if err != nil {
return err
}
rawValues[i] = value
}
features, _ := slice.SliceFromRawValues(
dataset.allFeaturesFloats,
dataset.featureColumnIndices,
dataset.columnTypes,
rawValues,
)
target, _ := slice.SliceFromRawValues(
dataset.allTargetsFloats,
dataset.targetColumnIndices,
dataset.columnTypes,
rawValues,
)
dataset.rows = append(dataset.rows, row.NewRow(features, target, dataset.numFeatures))
return nil
}
columnTypes, err = columntype.StringsToColumnTypes([]string{"hi", "0", "0"})
Ω(err).ShouldNot(HaveOccurred())
emptyTarget, err = slice.SliceFromRawValues(true, []int{}, columnTypes, []float64{})
Ω(err).ShouldNot(HaveOccurred())
helloRaw, err = columnTypes[0].PersistRawFromString("hello")
Ω(err).ShouldNot(HaveOccurred())
})
Context("When the classifier hasn't been trained", func() {
BeforeEach(func() {
features, err := slice.SliceFromRawValues(true, []int{1}, columnTypes, []float64{helloRaw, 1, 2})
Ω(err).ShouldNot(HaveOccurred())
testRow = row.NewRow(features, emptyTarget, 1)
})
It("Returns an error", func() {
_, err := kNNClassifier.Classify(testRow)
Ω(err).Should(HaveOccurred())
Ω(err).Should(BeAssignableToTypeOf(knnerrors.UntrainedClassifierError{}))
})
})
Context("When the classifier has been trained", func() {
BeforeEach(func() {
trainingData := dataset.NewDataset([]int{1, 2}, []int{0}, columnTypes)
err = trainingData.AddRowFromStrings([]string{"hello", "0", "0"})
Ω(err).ShouldNot(HaveOccurred())