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Flip vertical. Select a shape None. You must apply scaling within kernel. Example: 'KernelScale','auto'. Data Types: double single char string. Polynomial kernel function order, specified as the comma-separated pair consisting of 'PolynomialOrder' and a positive integer. If you set 'PolynomialOrder' and KernelFunction is not 'polynomial' , then the software throws an error.
Example: 'PolynomialOrder',2. Kernel offset parameter, specified as the comma-separated pair consisting of 'KernelOffset' and a nonnegative scalar. Example: 'KernelOffset',0. Flag to standardize the predictor data, specified as the comma-separated pair consisting of 'Standardize' and true 1 or false 0. The software centers and scales each predictor variable X or Tbl by the corresponding weighted column mean and standard deviation.
For details on weighted standardizing, see Algorithms. MATLAB does not standardize the data contained in the dummy variable columns generated for categorical predictors. The software trains the classifier using the standardized predictors, but stores the unstandardized predictors as a matrix or table in the classifier property X.
Example: 'Standardize',true. Optimization routine, specified as the comma-separated pair consisting of 'Solver' and a value in this table. Initial estimates of alpha coefficients, specified as the comma-separated pair consisting of 'Alpha' and a numeric vector of nonnegative values.
The length of Alpha must be equal to the number of rows in X. Each element of 'Alpha' corresponds to an observation in X. If you specify 'Alpha' and any one of the cross-validation name-value pair arguments 'CrossVal' , 'CVPartition' , 'Holdout' , 'KFold' , or 'Leaveout' , then the software returns an error.
If Y contains any missing values, then remove all rows of Y , X , and 'Alpha' that correspond to the missing values. Example: 'Alpha',0. Cache size, specified as the comma-separated pair consisting of 'CacheSize' and 'maximal' or a positive scalar. If CacheSize is 'maximal' , then the software reserves enough memory to hold the entire n -by- n Gram matrix. If CacheSize is a positive scalar, then the software reserves CacheSize megabytes of memory for training the model. Example: 'CacheSize','maximal'.
Flag to clip alpha coefficients, specified as the comma-separated pair consisting of 'ClipAlphas' and either true or false. Example: 'ClipAlphas',false. Nu must be greater than 0 and at most 1.
Set Nu to control the tradeoff between ensuring that most training examples are in the positive class and minimizing the weights in the score function.
Example: 'Nu',0. Number of iterations between optimization diagnostic message output, specified as the comma-separated pair consisting of 'NumPrint' and a nonnegative integer.
Example: 'NumPrint', Expected proportion of outliers in the training data, specified as the comma-separated pair consisting of 'OutlierFraction' and a numeric scalar in the interval [0,1. Suppose that you set 'OutlierFraction',outlierfraction , where outlierfraction is a value greater than 0. For two-class learning, the software implements robust learning.
The removed observations correspond to gradients that are large in magnitude. For one-class learning, the software finds an appropriate bias term such that outlierfraction of the observations in the training set have negative scores. Example: 'OutlierFraction',0. Flag to replace duplicate observations with single observations in the training data, specified as the comma-separated pair consisting of 'RemoveDuplicates' and true or false.
If RemoveDuplicates is true , then fitcsvm replaces duplicate observations in the training data with a single observation of the same value. The weight of the single observation is equal to the sum of the weights of the corresponding removed duplicates see Weights. If your data set contains many duplicate observations, then specifying 'RemoveDuplicates',true can decrease convergence time considerably.
Verbosity level, specified as the comma-separated pair consisting of 'Verbose' and 0 , 1 , or 2. The value of Verbose controls the amount of optimization information that the software displays in the Command Window and saves the information as a structure to Mdl. Example: 'Verbose',1. Each entry in the vector is an index value indicating that the corresponding predictor is categorical. The index values are between 1 and p , where p is the number of predictors used to train the model.
If fitcsvm uses a subset of input variables as predictors, then the function indexes the predictors using only the subset. The CategoricalPredictors values do not count the response variable, observation weight variable, or any other variables that the function does not use.
A true entry means that the corresponding predictor is categorical. The length of the vector is p. By default, if the predictor data is in a table Tbl , fitcsvm assumes that a variable is categorical if it is a logical vector, categorical vector, character array, string array, or cell array of character vectors.
If the predictor data is a matrix X , fitcsvm assumes that all predictors are continuous. To identify any other predictors as categorical predictors, specify them by using the 'CategoricalPredictors' name-value argument. For the identified categorical predictors, fitcsvm creates dummy variables using two different schemes, depending on whether a categorical variable is unordered or ordered. For an unordered categorical variable, fitcsvm creates one dummy variable for each level of the categorical variable.
For an ordered categorical variable, fitcsvm creates one less dummy variable than the number of categories. For details, see Automatic Creation of Dummy Variables. Example: 'CategoricalPredictors','all'. Data Types: single double logical char string cell. Names of classes to use for two-class learning, specified as a categorical, character, or string array; a logical or numeric vector; or a cell array of character vectors. ClassNames must have the same data type as the response variable in Tbl or Y.
If ClassNames is a character array, then each element must correspond to one row of the array. Specify the order of any input or output argument dimension that corresponds to the class order. For example, use ClassNames to specify the order of the dimensions of Cost or the column order of classification scores returned by predict.
Select a subset of classes for training. For example, suppose that the set of all distinct class names in Y is ["a","b","c"]. To train the model using observations from classes "a" and "c" only, specify "ClassNames",["a","c"]. The default value for ClassNames is the set of all distinct class names in the response variable in Tbl or Y.
Example: "ClassNames",["b","g"]. Misclassification cost for two-class learning, specified as the comma-separated pair consisting of 'Cost' and a square matrix or structure array. If you specify the square matrix Cost and the true class of an observation is i , then Cost i,j is the cost of classifying a point into class j. That is, rows correspond to the true classes and columns correspond to predicted classes.
To specify the class order for the corresponding rows and columns of Cost , also specify the ClassNames name-value pair argument. If you specify the structure S , then it must have two fields:. ClassNames , which contains the class names as a variable of the same data type as Y. ClassificationCosts , which contains the cost matrix with rows and columns ordered as in S. If you specify a cost matrix, then the software updates the prior probabilities by incorporating the penalties described in the cost matrix.
Consequently, the cost matrix resets to the default. Example: 'Cost',[0,1;2,0]. Data Types: double single struct. Predictor variable names, specified as a string array of unique names or cell array of unique character vectors. The functionality of PredictorNames depends on the way you supply the training data. If you supply X and Y , then you can use PredictorNames to assign names to the predictor variables in X.
The order of the names in PredictorNames must correspond to the column order of X. Also, size X,2 and numel PredictorNames must be equal. If you supply Tbl , then you can use PredictorNames to choose which predictor variables to use in training. That is, fitcsvm uses only the predictor variables in PredictorNames and the response variable during training. PredictorNames must be a subset of Tbl. VariableNames and cannot include the name of the response variable.
By default, PredictorNames contains the names of all predictor variables. A good practice is to specify the predictors for training using either PredictorNames or formula , but not both. Data Types: string cell. Prior probability for each class for two-class learning, specified as the comma-separated pair consisting of 'Prior' and a value in this table.
A structure S with two fields:. ClassNames contains the class names as a variable of the same type as Y. ClassProbs contains a vector of corresponding prior probabilities. The software normalizes the elements of the vector to sum to 1. Data Types: char string double single struct. If you supply Y , then you can use ResponseName to specify a name for the response variable. Example: "ResponseName","response". Score transformation, specified as a character vector, string scalar, or function handle.
The function handle must accept a matrix the original scores and return a matrix of the same size the transformed scores. Example: "ScoreTransform","logit". Observation weights, specified as the comma-separated pair consisting of 'Weights' and a numeric vector of positive values or the name of a variable in Tbl. The software weighs the observations in each row of X or Tbl with the corresponding value in Weights.
The size of Weights must equal the number of rows in X or Tbl. If you specify the input data as a table Tbl , then Weights can be the name of a variable in Tbl that contains a numeric vector. In this case, you must specify Weights as a character vector or string scalar. For example, if the weights vector W is stored as Tbl. W , then specify it as 'W'. Otherwise, the software treats all columns of Tbl , including W , as predictors or the response variable when training the model.
By default, Weights is ones n ,1 , where n is the number of observations in X or Tbl. The software normalizes Weights to sum up to the value of the prior probability in the respective class. You cannot use any cross-validation name-value argument together with the 'OptimizeHyperparameters' name-value argument.
You can modify the cross-validation for 'OptimizeHyperparameters' only by using the 'HyperparameterOptimizationOptions' name-value argument. Flag to train a cross-validated classifier, specified as the comma-separated pair consisting of 'Crossval' and 'on' or 'off'. If you specify 'on' , then the software trains a cross-validated classifier with 10 folds. You can use only one cross-validation name-value pair argument at a time to create a cross-validated model.
Alternatively, cross-validate later by passing Mdl to crossval. Example: 'Crossval','on'. Cross-validation partition, specified as a cvpartition partition object created by cvpartition. The partition object specifies the type of cross-validation and the indexing for the training and validation sets. To create a cross-validated model, you can specify only one of these four name-value arguments: CVPartition , Holdout , KFold , or Leaveout. Then, you can specify the cross-validated model by using 'CVPartition',cvp.
Fraction of the data used for holdout validation, specified as a scalar value in the range 0,1. If you specify 'Holdout',p , then the software completes these steps:. Store the compact, trained model in the Trained property of the cross-validated model. Example: 'Holdout',0. Number of folds to use in a cross-validated model, specified as a positive integer value greater than 1.
If you specify 'KFold',k , then the software completes these steps:. Stacked Polar Column Plot. Customization options include stacking bars of different "types", showing only one radial axis at degrees, and setting the radial axis to start from a non-zero value.
Fibonacci Sequences. The graph can be created by generating data with LabTalk scripts, plotting a theta X r Y polar graph and then mapping symbol size and color to the r and theta values. Polar contour with custom orientation and contour line. The polar contour plot is generated directly from a matrix data. Customizations for axes include setting angular axis orientation to clockwise from degrees and attaching the radial axis to the end angle.
Cropped polar chart with custom orientation and radian scale. Customization options for axes include changing the range and orientation of the angular axis , adding more radial axes and customizing their orientations, ticks, and labels, and cropping the graph to show only a portion of the data.
Two polar contour plots overlaid with one polar chart. This graph is created by merging three graphs, two polar contours surface temperature as a color-filled contour and sea level pressure as green contour lines and one polar chart the coastline of the Northern Hemisphere in two layers. Transparency is set to both the color-filled contour and the contour lines to make the bottom polar chart visible.
It is to show data in circular bars. A vector graph is a multidimensional graph used in industries such as meteorology, aviation, and construction that illustrates flow patterns e.
Customization options for vector include changing color, width and length and angle of arrow head, and adding a light source. This vector plot was created from data organized as X,Y, angle, and magnitude. The vector color was mapped to the magnitude values, illustrating the effect on river water flow around differently shaped pylons. The two pylons were drawn using circle object and a fill-area plot, added as a second layer.
An overlay plot depicting worldwide Ocean currents. The continents were plotted as a fill area graph, with the setting "inclusive broken by missing values" selected. The warm and cool currents were graphed as vector plots with labels data organized as X, Y, angle, and magnitude. The two graphs were then merged and superimposed.
Ternary diagrams are the graph type with applications primarily in the geosciences and civil engineering. The app provides you a few popular ternary plot templates to show the relative compositions of soils and rocks. Users can overlay scatters on the templates to show classification of soil and rocks. A 3D ternary surface with projection. The 3D ternary surface plot is created from a XYZZ data, while the projection at the bottom is the same surface flattened with only color-mapping contour line shown.
Ternary contour plot with data point overlay. The ternary contour is created from a XYZZ data, where the first XYZ is the ternary coordinates normalized between 0 and 1 while the second Z is used for color-mapping.
The same first XYZ is also added as a scatter plot on top. Ternary diagram of Soil Analysis Packing Fraction. Each line in this graph is created from a XYZ dataset stored in a separate worksheet. Customization options include labeling line plots with their corresponding packing fraction values and connecting data point with B-Spline line. Ternary phase diagram is popular in materials science community.
In this coordinate, Y and Z axes are both start at zero in same corner X. Schoeller diagram is used to show the relative concentrations of various anions and cations from multiple samples on a single graph. Piper diagram with TDS and point by point legend. Piper Diagrams contain 3 linked layers with normalized data. Customization options include adjusting the gap between ternary and rhomb, indexing symbol size and color, adding sample ID as label, and updating legend showing data location information point by point.
Origin allows you to create profiles on contour and image graphs, allowing easy inspection of vertical and horizontal cross-sections of your data. Contour profile plot. The contour profiles plot provides a dynamical way to analyze the contour data and generate profiles. Multiple horizontal, vertical or arbitrary profile lines can be added on the same contour. The thickness, color and position of the line are editable.
The line and projection along this line share the same color. The image profile plot provides a quick dynamical way of analyzing image data and generating profiles. Multiple horizontal, vertical or arbitrary profile lines can be added on the same image.
The thickness, color and position of the line are adjustable, while the line and projection along this line share the same color. Origin supports many types of function plots, including 2D and 3D parametric functions.
A color fill surface created by a build-in 3D parametric function plot breather, which is defined by a set of formulas. Frame planes are hidden and lighting effect is turned on. More 3D Parametric Function Plots.
All three planes and their associated axes are moved to original point, with plane grids and border turned off and directions of axes ticks changed to avoid overlapping with the surface. Lighting effect is turned on. Customizations include indexing symbol colors to identify different species, projecting scatter plot on XY plane, and setting transparency to the function plot surfaces so that they can be seen through.
Spherical contour plot of the probability distribution of the orientation of a protein domain regulatory light chain of myosin II in a muscle fibre. Spherical contour plot created by two 3D parametric function plots: One is a 3D colormap surface plot and another one is a 3D surface without colormap and only shows the mesh line. This is an image plot created from the stacked matrices to display the land surface temperature.
With browser panel is on, you can flip through to show temperature changes from to The graph can be created from an online template, Climate. OriginLab Corp. Graphing With over built-in graph types, Origin makes it easy to create and customize publication-quality graphs. Area Stacked Area Fill Area.
This graph displays three datasets with error bars. Lollipop plot of flowering duration data. Tetrahedral plot from XYZZ data. Share Facebook Pinterest Twitter 0.
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