/** * Returns the interval for the given confidence value. * * @param conf the confidence value in the interval [0, 1] * @return the interval */ public double[][] predictIntervals(double conf) { updateModel(); return m_MixtureModel.predictIntervals(conf); }
/** * Returns the quantile for the given percentage. * * @param percentage the percentage * @return the quantile */ public double predictQuantile(double percentage) { updateModel(); return m_MixtureModel.predictQuantile(percentage); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Add a new data value to the current estimator. * * @param data the new data value * @param given the new value that data is conditional upon * @param weight the weight assigned to the data value */ public void addValue(double data, double given, double weight) { m_Estimators[(int)data].addValue(given, weight); m_Weights.addValue((int)data, weight); }
/** * Returns textual description of this estimator. */ public String toString() { updateModel(); if (m_MixtureModel == null) { return ""; } return m_MixtureModel.toString(); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ @Override public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Add a new data value to the current estimator. * * @param data the new data value * @param given the new value that data is conditional upon * @param weight the weight assigned to the data value */ public void addValue(double data, double given, double weight) { m_Estimators[(int)data].addValue(given, weight); m_Weights.addValue((int)data, weight); }
/** * Returns the interval for the given confidence value. * * @param conf the confidence value in the interval [0, 1] * @return the interval */ public double[][] predictIntervals(double conf) { updateModel(); return m_MixtureModel.predictIntervals(conf); }
/** * Returns the quantile for the given percentage. * * @param percentage the percentage * @return the quantile */ public double predictQuantile(double percentage) { updateModel(); return m_MixtureModel.predictQuantile(percentage); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Add a new data value to the current estimator. * * @param data the new data value * @param given the new value that data is conditional upon * @param weight the weight assigned to the data value */ public void addValue(double data, double given, double weight) { m_Estimators[(int)data].addValue(given, weight); m_Weights.addValue((int)data, weight); }
/** * Returns textual description of this estimator. */ public String toString() { updateModel(); if (m_MixtureModel == null) { return ""; } return m_MixtureModel.toString(); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }
/** * Get a probability estimate for a value * * @param data the value to estimate the probability of * @param given the new value that data is conditional upon * @return the estimated probability of the supplied value */ public double getProbability(double data, double given) { return getEstimator(given).getProbability(data); }