class MIError2 |
Class for evaluating the correlation between two random variables representing errors
Random variables are discretized dividing their range values in bins.
![]() | MIError2 (unsigned num_data, unsigned num_bins, float** thedata, float* binlimits = 0, float min_v = 0.0, float max_v = 1.0) Constructor. |
![]() | MIError2 (unsigned num_data, unsigned num_bins, char* f, unsigned first, unsigned second, float* binlimits = 0, float min_v = MIN_VALUE, float max_v = MAX_VALUE) Constructor. |
![]() | ~MIError2 () Destroyer |
![]() | Calc_MICorrect2 (void) It calculates the component of mutual information generated by no errors or not correlated errors. |
![]() | Calc_MIErr2 (void) It calculates the component of mutual information generated by correlated errors. |
![]() | Print_MICorrect2 (void) Print to stdout the mutual information MICorrect2. |
![]() | Print_MIErr2 (void) Print to stdout the mutual information MIErr2. |
![]() | Calc_MICorrect (void) It calculates the component of mutual information generated by no errors Considering the matrix of the element of the mutual information between the discretized variables var1 and var2, is computed the mutual info corresponding to the first element of the matrix. |
![]() | Calc_MIErr (void) It calculates the component of mutual information generated by errors. |
![]() | Print_MICorrect (void) Print to stdout the mutual information MICorrect. |
![]() | Print_MIErr (void) Print to stdout the mutual information MIErr. |
Class for evaluating the correlation between two random variables representing errors
Random variables are discretized dividing their range values in bins. For each bin is computed its frequency, both for each single variable and the joint frequency. Methods can be used to evaluate the entropy of each variable, the joint entropy, the conditional entropy and the mutual information. The bins are not necessarily of the same size. The first bin correspond to correct values, the others to increasing errors. The input data must be errors between the stimated and the "true" values. The Mutual information error is calculated subtracting to the mutual information the mutal information generated by "correct" values or vlues with a single error.
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