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Data Classification
Clustering |
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Icon | Name | Description / Applications | Modules | |
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ClusterData_KMeans | Clusters data using KMeans algorithm. |
FoundationPro | ||
ClusterPoints2D | Clusters 2D points using K Means Clustering method. |
FoundationPro | ||
ClusterPoints2D_SingleLink | Clusters data using hierarchical single-link algorithm. |
FoundationPro | ||
ClusterPoints3D | Clusters 3D points using K Means Clustering method. |
FoundationPro | ||
FindConnectedComponents | Finds connected components in a graph given as set of bidirectional connections. |
FoundationPro | ||
Data Classification Common |
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Icon | Name | Description / Applications | Modules | |
CreateDataPartition | Divides the input set to test and train subsets, trying to maintain balance in class distribution. |
FoundationPro | ||
MeasureClassificationQuality_Binary | Calculates classification performance metrics for binary problems. |
FoundationPro | ||
MeasureClassificationQuality_Multiclass | Calculates classification performance metrics for multiclass problems. |
FoundationPro | ||
Multilayer Perceptron |
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Icon | Name | Description / Applications | Modules | |
MLP_Init | Creates multilayer perceptron model. |
FoundationPro | ||
MLP_Respond | Calculates multilayer perceptron answer. |
FoundationPro | ||
MLP_Train | Creates and trains multilayer perceptron classifier. |
FoundationPro | ||
Nearest Neighbors |
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Icon | Name | Description / Applications | Modules | |
KNN_Classify | Classify data using the KNN classifier. |
FoundationPro | ||
KNN_Init | Initializes the KNN classifier. |
FoundationPro | ||
KNN_Train | Trains KNN classifier using sample data. |
FoundationPro | ||
Principal Component Analysis |
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Icon | Name | Description / Applications | Modules | |
ApplyPCATransform | Applies previously obtained Principal Component Analysis (PCA) transformation coefficients to new data. |
FoundationPro | ||
CreatePCATransform | Performs the Principal Component Analysis (PCA) on provided data, creates the feature vector and normalization coefficients (mean and standard deviation of variables). |
FoundationPro | ||
MatrixDeterminant | Find the determinant of a square matrix. |
FoundationPro | ||
MatrixPseudoEigenvectors | Find the pseudo-eigenvalues and pseudo-eigenvectors of a symmetrical square matrix. |
FoundationPro | ||
NormalizeMatrixData | Treats Matrix as a data frame, where examples are in rows while columns represent features, and normalizes the data by subtracting mean from each column and dividing it by its standard deviation. |
FoundationPro | ||
ReversePCATransform | Reverses Principal Component Analysis (PCA) process. Can be used to transform data back to original feature space. |
FoundationPro | ||
Regression Analysis |
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Icon | Name | Description / Applications | Modules | |
LinearRegression | Computes linear regression of given point set. |
FoundationBasic | ||
LinearRegression_LTE | Computes linear regression of given point set using Least Trimmed Error algorithm. |
FoundationPro | ||
LinearRegression_M | Computes linear regression of given point set using selected M-estimator for outlier suppression. |
FoundationPro | ||
LinearRegression_RANSAC | Computes linear regression of given point set using RANSAC. |
FoundationPro | ||
LinearRegression_TheilSen | Computes linear regression of given point set using TheilSen algorithm. |
FoundationBasic | ||
QuadraticRegression | Computes quadratic regression of given point set. |
FoundationBasic | ||
QuadraticRegression_M | Computes quadratic regression of given point set using selected M-estimator for outlier suppression. |
FoundationPro | ||
QuadraticRegression_RANSAC | Computes quadratic regression of given point set using RANSAC. |
FoundationPro | ||
Statistics_OfArray | Computes basic statistical information out of an array of real numbers. The array must be not empty. |
FoundationBasic | ||
Statistics_OfLoop | Computes basic statistical information out of real numbers appearing in consecutive iterations. |
FoundationBasic | ||
Statistics |
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Icon | Name | Description / Applications | Modules | |
FindDataMode_FixedCount | Finds the mode in a set of data values by looking for highest concentration of a fixed number of samples. Can be used to determine a histogram maximum without actually creating the histogram. |
FoundationPro | ||
FindDataMode_FixedSpread | Finds the mode in a set of data values by looking for highest number of samples withing the specified spread. Can be used to determine a histogram maximum without actually creating the histogram. |
FoundationPro | ||
FindDataMode_MeanShift | Finds the mode in a set of data values by iteratively computing its median. Can be used to determine a histogram maximum without actually creating the histogram. |
FoundationPro | ||
FindDensityMaxima_FixedCount | Finds local density maxima in set of values by looking for the highest concentration of a fixed number of samples. Can be used to determine histogram's local maxima without actually creating the histogram. |
FoundationPro | ||
FindDensityMaxima_FixedSpread | Finds local density maxima in a set of values by looking for the highest number of samples withing a range determined by the given spread. Can be used to determine histogram's local maxima without actually creating the histogram. |
FoundationPro | ||
FindMatchingRegions_IoU | Finds corresponding regions in two arrays based on IoU value. |
FoundationPro | ||
RegionsIoU | Computes intersection over union value for two regions. |
FoundationPro | ||
TableOfConfusion_Basic | Computes statistics from a confusion matrix for given TP, FP, TN, FN. |
FoundationPro | ||
TableOfConfusion_BoolArray | Computes statistics from a confusion matrix for an array of groundTruth and results. |
FoundationPro | ||
TableOfConfusion_Histograms | Computes confusion matrix based on two histograms and threshold value. |
FoundationPro | ||
TableOfConfusion_Images | Computes statistics from a confusion matrix for image of groundTruth and results. |
FoundationPro | ||
Support Vector Machines |
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Icon | Name | Description / Applications | Modules | |
SVM_ClassifyMultiple | Classifies input points based on trained model. |
FoundationPro | ||
SVM_ClassifySingle | Classifies input features based on a trained model. |
FoundationPro | ||
SVM_Init | Initializes an SVM model. |
FoundationPro | ||
SVM_Train | Trains an SVM model. |
FoundationPro |