var searchIndex = {};
searchIndex["rusty_machine"] = {"doc":"# The rusty-machine crate.","items":[[0,"linalg","rusty_machine","Module for linear algebra.",null,null],[0,"matrix","rusty_machine::linalg","The matrix module.",null,null],[3,"Matrix","rusty_machine::linalg::matrix","The Matrix struct.",null,null],[11,"cholesky","","Cholesky decomposition",0,null],[11,"qr_decomp","","Compute the QR decomposition of the matrix.",0,null],[11,"upper_hessenberg","","Returns H, where H is the upper hessenberg form.",0,null],[11,"upper_hess_decomp","","Returns (U,H), where H is the upper hessenberg form\nand U is the unitary transform matrix.",0,null],[11,"eigenvalues","","Eigenvalues of a square matrix.",0,null],[11,"eigendecomp","","Eigen decomposition of a square matrix.",0,null],[11,"lup_decomp","","Computes L, U, and P for LUP decomposition.",0,null],[11,"fmt","","",0,null],[11,"new","","Constructor for Matrix struct.",0,{"inputs":[{"name":"usize"},{"name":"usize"},{"name":"vec"}],"output":{"name":"matrix"}}],[11,"rows","","Returns the number of rows in the Matrix.",0,null],[11,"cols","","Returns the number of columns in the Matrix.",0,null],[11,"data","","Returns a non-mutable reference to the underlying data.",0,null],[11,"mut_data","","Returns a mutable slice of the underlying data.",0,null],[11,"into_vec","","Consumes the Matrix and returns the Vec of data.",0,null],[11,"clone","","Clones the Matrix.",0,null],[11,"select_rows","","Select rows from matrix",0,null],[11,"select_cols","","Select columns from matrix",0,null],[11,"select","","Select block matrix from matrix",0,null],[11,"hcat","","Horizontally concatenates two matrices. With self on the left.",0,null],[11,"vcat","","Vertically concatenates two matrices. With self on top.",0,null],[11,"diag","","Extract the diagonal of the matrix",0,null],[11,"apply","","Applies a function to each element in the matrix.",0,null],[11,"zeros","","Constructs matrix of all zeros.",0,{"inputs":[{"name":"usize"},{"name":"usize"}],"output":{"name":"matrix"}}],[11,"ones","","Constructs matrix of all ones.",0,{"inputs":[{"name":"usize"},{"name":"usize"}],"output":{"name":"matrix"}}],[11,"identity","","Constructs the identity matrix.",0,{"inputs":[{"name":"usize"}],"output":{"name":"matrix"}}],[11,"from_diag","","Constructs matrix with given diagonal.",0,null],[11,"transpose","","Tranposes the given matrix",0,null],[11,"is_diag","","Checks if matrix is diagonal.",0,null],[11,"sum_rows","","The sum of the rows of the matrix.",0,null],[11,"sum_cols","","The sum of the columns of the matrix.",0,null],[11,"sum","","The sum of all elements in the matrix",0,null],[11,"elemul","","The elementwise product of two matrices.",0,null],[11,"elediv","","The elementwise division of two matrices.",0,null],[11,"mean","","The mean of the matrix along the specified axis.",0,null],[11,"variance","","The variance of the matrix along the specified axis.",0,null],[11,"solve","","Solves the equation Ax = y.",0,null],[11,"inverse","","Computes the inverse of the matrix.",0,null],[11,"det","","Computes the determinant of the matrix.",0,null],[11,"mul","","",0,null],[11,"mul","","",0,null],[11,"mul","","",0,null],[11,"mul","","",0,null],[11,"mul","","",0,null],[11,"mul","","",0,null],[11,"add","","",0,null],[11,"add","","",0,null],[11,"add","","",0,null],[11,"add","","",0,null],[11,"sub","","",0,null],[11,"sub","","",0,null],[11,"sub","","",0,null],[11,"sub","","",0,null],[11,"div","","",0,null],[11,"div","","",0,null],[11,"neg","","",0,null],[11,"index","","",0,null],[11,"norm","","Compute euclidean norm for matrix.",0,null],[11,"fmt","","Formats the Matrix for display.",0,null],[0,"vector","rusty_machine::linalg","The vector module.",null,null],[3,"Vector","rusty_machine::linalg::vector","The Vector struct.",null,null],[11,"fmt","","",1,null],[11,"new","","Constructor for Vector struct.",1,{"inputs":[{"name":"vec"}],"output":{"name":"vector"}}],[11,"size","","Returns the size of the Vector.",1,null],[11,"data","","Returns a non-mutable reference to the underlying data.",1,null],[11,"into_vec","","Consumes the Vector and returns the Vec of data.",1,null],[11,"clone","","Clones the Vector.",1,null],[11,"apply","","Applies a function to each element in the vector.",1,null],[11,"argmax","","Find the argmax of the Vector.",1,null],[11,"argmin","","Find the argmin of the Vector.",1,null],[11,"select","","Select elements from the Vector and form a new Vector from them.",1,null],[11,"zeros","","Constructs Vector of all zeros.",1,{"inputs":[{"name":"usize"}],"output":{"name":"vector"}}],[11,"ones","","Constructs Vector of all ones.",1,{"inputs":[{"name":"usize"}],"output":{"name":"vector"}}],[11,"dot","","Compute dot product with specified Vector.",1,null],[11,"sum","","The sum of the vector.",1,null],[11,"elemul","","The elementwise product of two vectors.",1,null],[11,"elediv","","The elementwise division of two vectors.",1,null],[11,"mean","","The mean of the vector.",1,null],[11,"variance","","The variance of the vector.",1,null],[11,"mul","","",1,null],[11,"mul","","",1,null],[11,"div","","",1,null],[11,"div","","",1,null],[11,"add","","",1,null],[11,"add","","",1,null],[11,"add","","",1,null],[11,"add","","",1,null],[11,"sub","","",1,null],[11,"sub","","",1,null],[11,"sub","","",1,null],[11,"sub","","",1,null],[11,"neg","","",1,null],[11,"index","","",1,null],[11,"norm","","Compute euclidean norm for vector.",1,null],[0,"utils","rusty_machine::linalg","Linear algebra utils module.",null,null],[5,"dot","rusty_machine::linalg::utils","Compute dot product of two slices.",null,null],[5,"unrolled_sum","","Unrolled sum",null,null],[5,"in_place_vec_bin_op","","Vectorized binary operation applied to two slices.\nThe first argument should be a mutable slice which will\nbe modified in place to prevent new memory allocation.",null,null],[5,"vec_sum","","Compute vector sum of two slices.",null,null],[5,"vec_sub","","Compute vector difference two slices.",null,null],[5,"ele_mul","","Computes elementwise multiplication.",null,null],[5,"ele_div","","Computes elementwise division.",null,null],[5,"argmax","","Find argmax of slice.",null,null],[5,"argmin","","Find argmin of slice.",null,null],[5,"find","","Find index of value in slice.",null,null],[0,"macros","rusty_machine::linalg","Macros for the linear algebra modules.",null,null],[8,"Metric","","Trait for linear algebra metrics.",null,null],[10,"norm","","Computes the euclidean norm.",2,null],[0,"learning","rusty_machine","Module for machine learning.",null,null],[0,"glm","rusty_machine::learning","Generalized Linear Model module",null,null],[3,"GenLinearModel","rusty_machine::learning::glm","The Generalized Linear Model",null,null],[3,"Logit","","The Logit link function.",null,null],[3,"Log","","The log link function.",null,null],[3,"Identity","","The Identity link function.",null,null],[3,"Bernoulli","","The Bernoulli regression family.",null,null],[3,"Binomial","","The Binomial regression family.",null,null],[3,"Normal","","The Normal regression family.",null,null],[3,"Poisson","","The Poisson regression family.",null,null],[8,"Criterion","","The criterion for the Generalized Linear Model.",null,null],[16,"Link","","The link function of the GLM Criterion.",3,null],[10,"model_variance","","The variance of the regression family.",3,null],[11,"initialize_mu","","Initializes the mean value.",3,null],[11,"compute_working_weight","","Computes the working weights that make up the diagonal\nof the `W` matrix used in the iterative reweighted least squares\nalgorithm.",3,null],[11,"compute_y_bar","","Computes the adjustment to the fitted values used during\nfitting.",3,null],[11,"apply_link_func","","Applies the link function to a vector.",3,null],[11,"apply_link_inv","","Applies the inverse of the link function to a vector.",3,null],[11,"link_grad","","Computes the gradient of the link function.",3,null],[8,"LinkFunc","","Link functions.",null,null],[10,"func","","The link function.",4,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[10,"func_grad","","The gradient of the link function.",4,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[10,"func_inv","","The inverse of the link function.\nOften called the 'mean' function.",4,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"fmt","","",5,null],[11,"new","","Constructs a new Generalized Linear Model.",5,{"inputs":[{"name":"c"}],"output":{"name":"genlinearmodel"}}],[11,"predict","","Predict output from inputs.",5,null],[11,"train","","Train the model using inputs and targets.",5,null],[11,"fmt","","",6,null],[11,"clone","","",6,null],[11,"func","","",6,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_grad","","",6,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_inv","","",6,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"fmt","","",7,null],[11,"clone","","",7,null],[11,"func","","",7,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_grad","","",7,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_inv","","",7,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"fmt","","",8,null],[11,"clone","","",8,null],[11,"func","","",8,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_grad","","",8,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_inv","","",8,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"fmt","","",9,null],[11,"clone","","",9,null],[11,"model_variance","","",9,null],[11,"initialize_mu","","",9,null],[11,"compute_working_weight","","",9,null],[11,"compute_y_bar","","",9,null],[11,"fmt","","",10,null],[11,"model_variance","","",10,null],[11,"initialize_mu","","",10,null],[11,"compute_working_weight","","",10,null],[11,"compute_y_bar","","",10,null],[11,"fmt","","",11,null],[11,"clone","","",11,null],[11,"model_variance","","",11,null],[11,"fmt","","",12,null],[11,"clone","","",12,null],[11,"model_variance","","",12,null],[11,"initialize_mu","","",12,null],[11,"compute_working_weight","","",12,null],[0,"gmm","rusty_machine::learning","Gaussian Mixture Models",null,null],[3,"GaussianMixtureModel","rusty_machine::learning::gmm","A Gaussian Mixture Model",null,null],[12,"cov_option","","The covariance options for the GMM.",13,null],[4,"CovOption","","Covariance options for GMMs.",null,null],[13,"Full","","The full covariance structure.",14,null],[13,"Regularized","","Adds a regularization constant to the covariance diagonal.",14,null],[13,"Diagonal","","Only the diagonal covariance structure.",14,null],[11,"fmt","","",14,null],[11,"clone","","",14,null],[11,"fmt","","",13,null],[11,"train","","Train the model using inputs.",13,null],[11,"predict","","Predict output from inputs.",13,null],[11,"new","","Constructs a new Gaussian Mixture Model",13,{"inputs":[{"name":"usize"}],"output":{"name":"gaussianmixturemodel"}}],[11,"set_max_iters","","Sets the max number of iterations for the EM algorithm.",13,null],[0,"lin_reg","rusty_machine::learning","Linear Regression module",null,null],[3,"LinRegressor","rusty_machine::learning::lin_reg","Linear Regression Model.",null,null],[11,"fmt","","",15,null],[11,"default","","",15,{"inputs":[],"output":{"name":"linregressor"}}],[11,"parameters","","Get the parameters from the model.",15,null],[11,"train","","Train the linear regression model.",15,null],[11,"predict","","Predict output value from input data.",15,null],[11,"compute_grad","","",15,null],[11,"train_with_optimization","","Train the linear regressor using Gradient Descent.",15,null],[0,"logistic_reg","rusty_machine::learning","Logistic Regression module",null,null],[3,"LogisticRegressor","rusty_machine::learning::logistic_reg","Logistic Regression Model.",null,null],[3,"BaseLogisticRegressor","","The Base Logistic Regression model.",null,null],[11,"fmt","","",16,null],[11,"default","","",16,{"inputs":[],"output":{"name":"logisticregressor"}}],[11,"new","","Constructs untrained logistic regression model.",16,{"inputs":[{"name":"a"}],"output":{"name":"logisticregressor"}}],[11,"parameters","","Get the parameters from the model.",16,null],[11,"train","","Train the logistic regression model.",16,null],[11,"predict","","Predict output value from input data.",16,null],[11,"fmt","","",17,null],[11,"compute_grad","","",17,null],[0,"k_means","rusty_machine::learning","K-means Classification",null,null],[3,"KMeansClassifier","rusty_machine::learning::k_means","K-Means Classification model.",null,null],[12,"iters","","Max iterations of algorithm to run.",18,null],[12,"k","","The number of classes.",18,null],[12,"centroids","","The fitted centroids .",18,null],[12,"init_algorithm","","The initial algorithm to use.",18,null],[4,"InitAlgorithm","","Initialization Algorithm enum.",null,null],[13,"Forgy","","The Forgy initialization scheme.",19,null],[13,"RandomPartition","","The Random Partition initialization scheme.",19,null],[13,"KPlusPlus","","The K-means ++ initialization scheme.",19,null],[11,"fmt","","",19,null],[11,"clone","","",19,null],[11,"fmt","","",18,null],[11,"predict","","Predict classes from data.",18,null],[11,"train","","Train the classifier using input data.",18,null],[11,"new","","Constructs untrained k-means classifier model.",18,{"inputs":[{"name":"usize"}],"output":{"name":"kmeansclassifier"}}],[0,"nnet","rusty_machine::learning","Neural Network module",null,null],[3,"NeuralNet","rusty_machine::learning::nnet","Neural Network Model",null,null],[3,"BaseNeuralNet","","Base Neural Network struct",null,null],[3,"BCECriterion","","The binary cross entropy criterion.",null,null],[3,"MSECriterion","","The mean squared error criterion.",null,null],[8,"Criterion","","Criterion for Neural Networks",null,null],[16,"ActFunc","","The activation function for the criterion.",20,null],[16,"Cost","","The cost function for the criterion.",20,null],[11,"activate","","The activation function applied to a matrix.",20,null],[11,"grad_activ","","The gradient of the activation function applied to a matrix.",20,null],[11,"cost","","The cost function.",20,null],[11,"cost_grad","","The gradient of the cost function.",20,null],[11,"fmt","","",21,null],[11,"predict","","Predict neural network output using forward propagation.",21,null],[11,"train","","Train the model using gradient optimization and back propagation.",21,null],[11,"default","","Creates a neural network with the specified layer sizes.",21,null],[11,"new","","Create a new neural network with the specified layer sizes.",21,null],[11,"get_net_weights","","Gets matrix of weights between specified layer and forward layer.",21,null],[11,"fmt","","",22,null],[11,"compute_grad","","Compute the gradient of the neural network.",22,null],[11,"fmt","","",23,null],[11,"clone","","",23,null],[11,"fmt","","",24,null],[11,"clone","","",24,null],[0,"gp","rusty_machine::learning","Gaussian Processes",null,null],[3,"ConstMean","rusty_machine::learning::gp","Constant mean function",null,null],[3,"GaussianProcess","","Gaussian Process struct",null,null],[12,"noise","","The observation noise of the GP.",25,null],[8,"MeanFunc","","Trait for GP mean functions.",null,null],[10,"func","","Compute the mean function applied elementwise to a matrix.",26,null],[11,"fmt","","",27,null],[11,"clone","","",27,null],[11,"default","","",27,{"inputs":[],"output":{"name":"constmean"}}],[11,"func","","",27,null],[11,"fmt","","",25,null],[11,"default","","",25,{"inputs":[],"output":{"name":"gaussianprocess"}}],[11,"new","","Construct a new Gaussian Process.",25,{"inputs":[{"name":"t"},{"name":"u"},{"name":"f64"}],"output":{"name":"gaussianprocess"}}],[11,"predict","","Predict output from inputs.",25,null],[11,"train","","Train the model using data and outputs.",25,null],[11,"get_posterior","","Compute the posterior distribution [UNSTABLE]",25,null],[0,"svm","rusty_machine::learning","Support Vector Machine Module",null,null],[3,"SVM","rusty_machine::learning::svm","Support Vector Machine",null,null],[12,"optim_iters","","Number of iterations for training.",28,null],[11,"fmt","","",28,null],[11,"default","","",28,{"inputs":[],"output":{"name":"svm"}}],[11,"new","","Constructs an untrained SVM with specified\nkernel and lambda which determins the hardness\nof the margin.",28,{"inputs":[{"name":"k"},{"name":"f64"}],"output":{"name":"svm"}}],[11,"predict","","",28,null],[11,"train","","",28,null],[0,"optim","rusty_machine::learning","Module for optimization in machine learning setting.",null,null],[0,"grad_desc","rusty_machine::learning::optim","Gradient Descent",null,null],[3,"GradientDesc","rusty_machine::learning::optim::grad_desc","Batch Gradient Descent algorithm",null,null],[12,"alpha","","The step-size for the gradient descent steps.",29,null],[12,"iters","","The number of iterations to run.",29,null],[3,"StochasticGD","","Stochastic Gradient Descent algorithm.",null,null],[12,"alpha","","Controls the momentum of the descent",30,null],[12,"mu","","The square root of the raw learning rate.",30,null],[12,"iters","","The number of passes through the data.",30,null],[11,"fmt","","",29,null],[11,"clone","","",29,null],[11,"default","","",29,{"inputs":[],"output":{"name":"gradientdesc"}}],[11,"new","","Construct a gradient descent algorithm.",29,{"inputs":[{"name":"f64"},{"name":"usize"}],"output":{"name":"gradientdesc"}}],[11,"optimize","","",29,null],[11,"fmt","","",30,null],[11,"clone","","",30,null],[11,"default","","",30,{"inputs":[],"output":{"name":"stochasticgd"}}],[11,"new","","Construct a stochastic gradient descent algorithm.",30,{"inputs":[{"name":"f64"},{"name":"f64"},{"name":"usize"}],"output":{"name":"stochasticgd"}}],[11,"optimize","","",30,null],[0,"fmincg","rusty_machine::learning::optim","Module for the fmincg optimization algorithm.",null,null],[3,"ConjugateGD","rusty_machine::learning::optim::fmincg","Conjugate Gradient Descent algorithm",null,null],[12,"rho","","Constant in the Wolfe-Powell conditions.",31,null],[12,"sig","","Constant in the Wolfe-Powell conditions.",31,null],[12,"int","","Don't reevaluate within `int` of the limit of the current bracket.",31,null],[12,"ext","","Extrapolate max of `ext` times the current bracket.",31,null],[12,"max","","Max of `max` function evaluations per line search",31,null],[12,"ratio","","The maximum allowed slope ratio",31,null],[12,"iters","","The default number of max iterations.",31,null],[11,"fmt","","",31,null],[11,"clone","","",31,null],[11,"default","","",31,{"inputs":[],"output":{"name":"conjugategd"}}],[11,"optimize","","",31,null],[8,"Optimizable","rusty_machine::learning::optim","Trait for models which can be gradient-optimized.",null,null],[16,"Inputs","","The input data type to the model.",32,null],[16,"Targets","","The target data type to the model.",32,null],[10,"compute_grad","","Compute the gradient for the model.",32,null],[8,"OptimAlgorithm","","Trait for optimization algorithms.",null,null],[10,"optimize","","Return the optimized parameter using gradient optimization.",33,null],[0,"toolkit","rusty_machine::learning","Module for learning tools.",null,null],[0,"activ_fn","rusty_machine::learning::toolkit","Activation Functions.",null,null],[3,"Sigmoid","rusty_machine::learning::toolkit::activ_fn","Sigmoid activation function.",null,null],[3,"Linear","","Linear activation function.",null,null],[3,"Exp","","Exponential activation function.",null,null],[8,"ActivationFunc","","Trait for activation functions in models.",null,null],[10,"func","","The activation function.",34,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[10,"func_grad","","The gradient of the activation function.",34,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[10,"func_inv","","The inverse of the activation function.",34,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"fmt","","",35,null],[11,"clone","","",35,null],[11,"func","","Sigmoid function.",35,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_grad","","Gradient of sigmoid function.",35,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_inv","","",35,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"fmt","","",36,null],[11,"clone","","",36,null],[11,"func","","",36,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_grad","","",36,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_inv","","",36,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"fmt","","",37,null],[11,"clone","","",37,null],[11,"func","","",37,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_grad","","",37,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[11,"func_inv","","",37,{"inputs":[{"name":"f64"}],"output":{"name":"f64"}}],[0,"kernel","rusty_machine::learning::toolkit","Module for kernels",null,null],[3,"KernelSum","rusty_machine::learning::toolkit::kernel","The sum of two kernels",null,null],[3,"KernelProd","","The pointwise product of two kernels",null,null],[3,"KernelArith","","A wrapper tuple struct used for kernel arithmetic",null,null],[12,"0","","",38,null],[3,"Linear","","The Linear Kernel",null,null],[12,"c","","Constant term added to inner product.",39,null],[3,"Polynomial","","The Polynomial Kernel",null,null],[12,"alpha","","Scaling of the inner product.",40,null],[12,"c","","Constant added to inner product.",40,null],[12,"d","","The power to raise the sum to.",40,null],[3,"SquaredExp","","Squared exponential kernel",null,null],[12,"ls","","The length scale of the kernel.",41,null],[12,"ampl","","The amplitude of the kernel.",41,null],[3,"Exponential","","The Exponential Kernel",null,null],[12,"ls","","The length scale of the kernel.",42,null],[12,"ampl","","The amplitude of the kernel.",42,null],[3,"HyperTan","","The Hyperbolic Tangent Kernel.",null,null],[12,"alpha","","The scaling of the inner product.",43,null],[12,"c","","The constant to add to the inner product.",43,null],[3,"Multiquadric","","The Multiquadric Kernel.",null,null],[12,"c","","Constant added to square of difference.",44,null],[3,"RationalQuadratic","","The Rational Quadratic Kernel.",null,null],[12,"alpha","","Controls inverse power and difference scale.",45,null],[12,"ls","","Length scale controls scale of difference.",45,null],[8,"Kernel","","The Kernel trait",null,null],[10,"kernel","","The kernel function.",46,null],[11,"fmt","","",47,null],[11,"kernel","","",47,null],[11,"fmt","","",48,null],[11,"kernel","","",48,null],[11,"fmt","","",38,null],[11,"add","","",38,null],[11,"mul","","",38,null],[11,"fmt","","",39,null],[11,"clone","","",39,null],[11,"new","","Constructs a new Linear Kernel.",39,{"inputs":[{"name":"f64"}],"output":{"name":"linear"}}],[11,"default","","",39,{"inputs":[],"output":{"name":"linear"}}],[11,"kernel","","",39,null],[11,"fmt","","",40,null],[11,"clone","","",40,null],[11,"new","","Constructs a new Polynomial Kernel.",40,{"inputs":[{"name":"f64"},{"name":"f64"},{"name":"f64"}],"output":{"name":"polynomial"}}],[11,"default","","",40,{"inputs":[],"output":{"name":"polynomial"}}],[11,"kernel","","",40,null],[11,"fmt","","",41,null],[11,"clone","","",41,null],[11,"new","","Construct a new squared exponential kernel.",41,{"inputs":[{"name":"f64"},{"name":"f64"}],"output":{"name":"squaredexp"}}],[11,"default","","",41,{"inputs":[],"output":{"name":"squaredexp"}}],[11,"kernel","","The squared exponential kernel function.",41,null],[11,"fmt","","",42,null],[11,"clone","","",42,null],[11,"new","","Construct a new squared exponential kernel.",42,{"inputs":[{"name":"f64"},{"name":"f64"}],"output":{"name":"exponential"}}],[11,"default","","",42,{"inputs":[],"output":{"name":"exponential"}}],[11,"kernel","","The squared exponential kernel function.",42,null],[11,"fmt","","",43,null],[11,"clone","","",43,null],[11,"new","","Constructs a new Hyperbolic Tangent Kernel.",43,{"inputs":[{"name":"f64"},{"name":"f64"}],"output":{"name":"hypertan"}}],[11,"default","","",43,{"inputs":[],"output":{"name":"hypertan"}}],[11,"kernel","","",43,null],[11,"fmt","","",44,null],[11,"clone","","",44,null],[11,"new","","Constructs a new Multiquadric Kernel.",44,{"inputs":[{"name":"f64"}],"output":{"name":"multiquadric"}}],[11,"default","","",44,{"inputs":[],"output":{"name":"multiquadric"}}],[11,"kernel","","",44,null],[11,"fmt","","",45,null],[11,"clone","","",45,null],[11,"new","","Constructs a new Rational Quadratic Kernel.",45,{"inputs":[{"name":"f64"},{"name":"f64"}],"output":{"name":"rationalquadratic"}}],[11,"default","","",45,{"inputs":[],"output":{"name":"rationalquadratic"}}],[11,"kernel","","",45,null],[0,"cost_fn","rusty_machine::learning::toolkit","Cost Functions.",null,null],[3,"MeanSqError","rusty_machine::learning::toolkit::cost_fn","The mean squared error cost function.",null,null],[3,"CrossEntropyError","","The cross entropy error cost function.",null,null],[8,"CostFunc","","Trait for cost functions in models.",null,null],[10,"cost","","The cost function.",49,{"inputs":[{"name":"t"},{"name":"t"}],"output":{"name":"f64"}}],[10,"grad_cost","","The gradient of the cost 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functions for random sampling",null,null],[5,"reservoir_sample","rusty_machine::learning::toolkit::rand_utils","```\nuse rusty_machine::learning::toolkit::rand_utils;",null,null],[8,"SupModel","rusty_machine::learning","Trait for supervised model.",null,null],[10,"predict","","Predict output from inputs.",52,null],[10,"train","","Train the model using inputs and targets.",52,null],[8,"UnSupModel","","Trait for unsupervised model.",null,null],[10,"predict","","Predict output from inputs.",53,null],[10,"train","","Train the model using 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