Linearalgebra — Penunjuk dan Strategi

regressions Library "regressions" This library computes least square regression models for polynomials of any form for a given data set of x and y values. fit(X, y, reg_type, degrees) Takes a list of X and y values and the degrees of the polynomial and returns a least square regression for the given polynomial on the dataset. Parameters: X (array) : (float ) X inputs for regression fit. y (array) : (float ) y outputs for regression fit. reg_type (string) : (string) The type of regression. If passing value for degrees use reg.type_custom degrees (array) : (int ) The degrees of the polynomial which will be fit to the data. ex: passing array.from(0, 3) would be a polynomial of form c1x^0 + c2x^3 where c2 and c1 will be coefficients of the best fitting polynomial. Returns: (regression) returns a regression with the best fitting coefficients for the selecected polynomial regress(reg, x) Regress one x input. Parameters: reg (regression) : (regression) The fitted regression which the y_pred will be calulated with. x (float) : (float) The input value cooresponding to the y_pred. Returns: (float) The best fit y value for the given x input and regression. predict(reg, X) Predict a new set of X values with a fitted regression. -1 is one bar ahead of the realtime Parameters: reg (regression) : (regression) The fitted regression which the y_pred will be calulated with. X (array) Returns: (float ) The best fit y values for the given x input and regression. generate_points(reg, x, y, left_index, right_index) Takes a regression object and creates chart points which can be used for plotting visuals like lines and labels. Parameters: reg (regression) : (regression) Regression which has been fitted to a data set. x (array) : (float ) x values which coorispond to passed y values y (array) : (float ) y values which coorispond to passed x values left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value. right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value. Returns: (chart.point ) Returns an array of chart points plot_reg(reg, x, y, left_index, right_index, curved, close, line_color, line_width) Simple plotting function for regression for more custom plotting use generate_points() to create points then create your own plotting function. Parameters: reg (regression) : (regression) Regression which has been fitted to a data set. x (array) y (array) left_index (int) : (int) The offset of the bar farthest to the realtime bar should be larger than left_index value. right_index (int) : (int) The offset of the bar closest to the realtime bar should be less than right_index value. curved (bool) : (bool) If the polyline is curved or not. close (bool) : (bool) If true the polyline will be closed. line_color (color) : (color) The color of the line. line_width (int) : (int) The width of the line. Returns: (polyline) The polyline for the regression. series_to_list(src, left_index, right_index) Convert a series to a list. Creates a list of all the cooresponding source values from left_index to right_index. This should be called at the highest scope for consistency. Parameters: src (float) : (float ) The source the list will be comprised of. left_index (int) : (float ) The left most bar (farthest back historical bar) which the cooresponding source value will be taken for. right_index (int) : (float ) The right most bar closest to the realtime bar which the cooresponding source value will be taken for. Returns: (float ) An array of size left_index-right_index range_list(start, stop, step) Creates an from the start value to the stop value. Parameters: start (int) : (float ) The true y values. stop (int) : (float ) The predicted y values. step (int) : (int) Positive integer. The spacing between the values. ex: start=1, stop=6, step=2: Returns: (float ) An array of size stop-start regression Fields: coeffs (array__float) degrees (array__float) type_linear (series__string) type_quadratic (series__string) type_cubic (series__string) type_custom (series__string) _squared_error (series__float) X (array__float)

FunctionLAPACKdsyrk Library "FunctionLAPACKdsyrk" subroutine part of LAPACK: Linear Algebra Package, performs one of the symmetric rank k operations . C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, . where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. . reference: netlib.org dsyrk(uplo, trans, n, k, alpha, a, lda, beta, c, ldc) performs one of the symmetric rank k operations . C := alpha*A*A**T + beta*C, or C := alpha*A**T*A + beta*C, . where alpha and beta are scalars, C is an n by n symmetric matrix and A is an n by k matrix in the first case and a k by n matrix in the second case. . Parameters: uplo : string specifies whether the upper or lower triangular part of the array C is to be referenced as follows: UPLO = 'U' or 'u' Only the upper triangular part of C is to be referenced. UPLO = 'L' or 'l' Only the lower triangular part of C is to be referenced. . trans : string specifies the operation to be performed as follows: TRANS = 'N' or 'n' C := alpha*A*A**T + beta*C. TRANS = 'T' or 't' C := alpha*A**T*A + beta*C. TRANS = 'C' or 'c' C := alpha*A**T*A + beta*C. . n : int specifies the order of the matrix C. N must be at least zero. k : int On entry with: TRANS = 'N' or 'n', K specifies the number of columns of the matrix A. TRANS = 'T' or 't' or 'C' or 'c', K specifies the number of rows of the matrix A. K must be at least zero. . alpha : float scalar. a : matrix matrix A. lda : int specifies the first dimension of A. beta : float scalar. c : matrix matrix C, is overwritten by the lower triangular part of the updated matrix. ldc : int specifies the first dimension of C Returns: void, C is overwritten by the lower triangular part of the updated matrix.

FunctionLAPACKdtrsm Library "FunctionLAPACKdtrsm" subroutine in the LAPACK:linear algebra package, used to solve one of the following matrix equations: op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B. reference: netlib.org dtrsm(side, uplo, transa, diag, m, n, alpha, a, lda, b, ldb) solves one of the matrix equations op( A )*X = alpha*B, or X*op( A ) = alpha*B, where alpha is a scalar, X and B are m by n matrices, A is a unit, or non-unit, upper or lower triangular matrix and op( A ) is one of op( A ) = A or op( A ) = A**T. The matrix X is overwritten on B. Parameters: side : string , On entry, SIDE specifies whether op( A ) appears on the left or right of X as follows: SIDE = 'L' or 'l' op( A )*X = alpha*B. SIDE = 'R' or 'r' X*op( A ) = alpha*B. uplo : string , specifies whether the matrix A is an upper or lower triangular matrix as follows: UPLO = 'U' or 'u' A is an upper triangular matrix. UPLO = 'L' or 'l' A is a lower triangular matrix. transa : string , specifies the form of op( A ) to be used in the matrix multiplication as follows: TRANSA = 'N' or 'n' op( A ) = A. TRANSA = 'T' or 't' op( A ) = A**T. TRANSA = 'C' or 'c' op( A ) = A**T. diag : string , specifies whether or not A is unit triangular as follows: DIAG = 'U' or 'u' A is assumed to be unit triangular. DIAG = 'N' or 'n' A is not assumed to be unit triangular. m : int , the number of rows of B. M must be at least zero. n : int , the number of columns of B. N must be at least zero. alpha : float , specifies the scalar alpha. When alpha is zero then A is not referenced and B need not be set before entry. a : matrix, Triangular matrix. lda : int , specifies the first dimension of A. b : matrix, right-hand side matrix B, and on exit is overwritten by the solution matrix X. ldb : int , specifies the first dimension of B. Returns: void, modifies matrix b. usage: dtrsm ('L', 'U', 'N', 'N', 5, 3, 1.0, a, 7, b, 6)