/** * Returns a textual description of this predictor applicable * to all sub classes. */ public String toString() { return Utils.doubleToString(m_coefficient, 12, 4) + " * "; }
/** * Display a representation of this estimator */ @Override public String toString() { return "Normal Distribution. Mean = " + Utils.doubleToString(m_Mean, 4) + " StandardDev = " + Utils.doubleToString(m_StandardDev, 4) + " WeightSum = " + Utils.doubleToString(m_SumOfWeights, 4) + " Precision = " + m_Precision + "\n"; }
@Override public String toString() { return m_classLabel + ": " + m_recordCount + " (" + Utils.doubleToString(m_confidence, 2) + ") "; } }
/** * Get this item's value as a String. * * @return this item's value as a String. */ @Override public String getItemValueAsString() { return Utils.doubleToString(m_splitPoint, 3); }
/** Display a representation of this estimator */ public String toString() { if (m_CovarianceInverse == null) { return "No covariance inverse\n"; } return "Mahalanovis Distribution. Mean = " + Utils.doubleToString(m_ValueMean, 4, 2) + " ConditionalOffset = " + Utils.doubleToString(m_ConstDelta, 4, 2) + "\n" + "Covariance Matrix: Determinant = " + m_Determinant + " Inverse:\n" + m_CovarianceInverse; }
@Override public String toString() { return m_classLabel + ": " + m_recordCount + " (" + Utils.doubleToString(m_confidence, 2) + ") "; } }
/** * Get a list of bin labels for this histogram * * @return a list of bin labels */ public List<String> getBinLabels() { List<String> labs = new ArrayList<String>(); for (double c : m_binCutpoints) { labs.add(Utils.doubleToString(c, 3) + "]"); } return labs; }
/** Display a representation of this estimator */ @Override public String toString() { if (m_Covariance == null) { calculateCovariance(); } String result = "NN Conditional Estimator. " + m_CondValues.size() + " data points. Mean = " + Utils.doubleToString(m_ValueMean, 4, 2) + " Conditional mean = " + Utils.doubleToString(m_CondMean, 4, 2); result += " Covariance Matrix: \n" + m_Covariance; return result; }
/** * Return a textual description of this predictor term. */ public String toString() { StringBuffer result = new StringBuffer(); result.append("(" + Utils.doubleToString(m_coefficient, 12, 4)); for (int i = 0; i < m_fieldNames.length; i++) { result.append(" * " + m_fieldNames[i]); } result.append(")"); return result.toString(); }
@Override public void actionPerformed(ActionEvent e) { if (m_costR.isSelected()) { m_costBenefitL.setText("Cost: "); } else { m_costBenefitL.setText("Benefit: "); } double gain = Double.parseDouble(m_gainV.getText()); gain = -gain; m_gainV.setText(Utils.doubleToString(gain, 2)); } };
@Override public void actionPerformed(ActionEvent e) { if (m_costR.isSelected()) { m_costBenefitL.setText("Cost: "); } else { m_costBenefitL.setText("Benefit: "); } double gain = Double.parseDouble(m_gainV.getText()); gain = -gain; m_gainV.setText(Utils.doubleToString(gain, 2)); } };
@Override public void actionPerformed(ActionEvent e) { if (m_costR.isSelected()) { m_costBenefitL.setText("Cost: "); } else { m_costBenefitL.setText("Benefit: "); } double gain = Double.parseDouble(m_gainV.getText()); gain = -gain; m_gainV.setText(Utils.doubleToString(gain, 2)); } };
public String toStringMetric(int premiseSupport, int consequenceSupport, int totalSupport, int totalTransactions) { return m_stringVal + ":(" + Utils.doubleToString(compute(premiseSupport, consequenceSupport, totalSupport, totalTransactions), 2) + ")"; }
public String toXML(int premiseSupport, int consequenceSupport, int totalSupport, int totalTransactions) { String result = "<CRITERE name=\"" + m_stringVal + "\" value=\" " + Utils.doubleToString(compute(premiseSupport, consequenceSupport, totalSupport, totalTransactions), 2) + "\"/>"; return result; } }
public String toXML(int premiseSupport, int consequenceSupport, int totalSupport, int totalTransactions) { String result = "<CRITERE name=\"" + m_stringVal + "\" value=\" " + Utils.doubleToString(compute(premiseSupport, consequenceSupport, totalSupport, totalTransactions), 2) + "\"/>"; return result; } }
/** * Prints this antecedent * * @return a textual description of this antecedent */ @Override public String toString() { String symbol = ((int) value == 0) ? " <= " : " >= "; return (att.name() + symbol + Utils.doubleToString(splitPoint, 6)); }
/** * Gets the upper and lower boundary for the range of x * * @return the string containing the upper and lower boundary for the range of * x, separated by .. */ protected String getRange() { String fromTo = "" + Utils.doubleToString(getMinRange(), 2) + ".." + Utils.doubleToString(getMaxRange(), 2); return fromTo; }
/** * Return a textual description of this predictor. */ public String toString() { String output = super.toString(); output += m_name; if (m_exponent > 1.0 || m_exponent < 1.0) { output += "^" + Utils.doubleToString(m_exponent, 4); } return output; }
/** * Quick test of timestamp * * @param args the commandline options */ public static void main(String[] args) { System.err.println(Utils.doubleToString(getTimestamp().doubleValue(), 4)); } } // CrossValidationResultProducer
/** * Prints this antecedent * * @return a textual description of this antecedent */ @Override public String toString() { String symbol = Utils.eq(value, 0.0) ? " <= " : " > "; return (att.name() + symbol + Utils.doubleToString(splitPoint, 6)); }