/** * Reads the binary representation of a learner with this object's run-time type, overwriting * any and all learned or manually specified parameters as well as the label lexicon but without * modifying the feature lexicon. * * @param in The input stream. **/ public void read(ExceptionlessInputStream in) { super.read(in); learningRate = in.readDouble(); bias = in.readDouble(); weightVector = SparseWeightVector.readWeightVector(in); }
/** * Reads the binary representation of a learner with this object's run-time type, overwriting * any and all learned or manually specified parameters as well as the label lexicon but without * modifying the feature lexicon. * * @param in The input stream. **/ public void read(ExceptionlessInputStream in) { super.read(in); int N = in.readInt(); network = new OVector(N); for (int i = 0; i < N; ++i) network.add(SparseWeightVector.readWeightVector(in)); }
/** * Reads the binary representation of a learner with this object's run-time type, overwriting * any and all learned or manually specified parameters as well as the label lexicon but without * modifying the feature lexicon. * * @param in The input stream. **/ public void read(ExceptionlessInputStream in) { super.read(in); confidence = in.readDouble(); initialVariance = in.readDouble(); variancesBias = in.readDouble(); variances = SparseWeightVector.readWeightVector(in); }
/** * Reads the binary representation of a learner with this object's run-time type, overwriting * any and all learned or manually specified parameters as well as the label lexicon but without * modifying the feature lexicon. * * @param in The input stream. **/ public void read(ExceptionlessInputStream in) { super.read(in); int N = in.readInt(); if (N == 0) allowableValues = null; else { allowableValues = new String[N]; for (int i = 0; i < N; ++i) allowableValues[i] = in.readString(); } initialWeight = in.readDouble(); threshold = in.readDouble(); learningRate = in.readDouble(); positiveThickness = in.readDouble(); negativeThickness = in.readDouble(); bias = in.readDouble(); weightVector = SparseWeightVector.readWeightVector(in); }