NMath User's Guide

TOC | Previous | Next | Index

Chapter 1. Overview

1.1 Product Components

1.2 Software Requirements

1.3 NMath Assemblies

1.4 NMath License Key

1.5 NMath Configuration

1.6 Building and Deploying NMath Applications

1.7 Web Applications

1.8 Very Large Objects

1.9 Documentation

1.10 Technical Support

Chapter 2. NMath Core

Chapter 3. Complex Number Types

3.1 Creating Complex Numbers

3.2 Value Operations on Complex Numbers

3.3 Logical Operations on Complex Numbers

3.4 Arithmetic Operations on Complex Numbers

3.5 Functions of Complex Numbers

Chapter 4. Viewing Data

4.1 DataBlock Classes

4.2 Slices and Ranges

Chapter 5. Vector Classes

5.1 Class Names

5.2 Creating Vectors

5.3 Value Operations on Vectors

5.4 Logical Operations on Vectors

5.5 Arithmetic Operations on Vectors

5.6 Functions of Vectors

5.7 Generic Functions

5.8 Vector Enumeration

Chapter 6. Matrix Classes

6.1 Class Names

6.2 Creating Matrices

6.3 Value Operations on Matrices

6.4 Logical Operations on Matrices

6.5 Arithmetic Operations on Matrices

6.6 Vector Views

6.7 Functions of Matrices

6.8 Generic Functions

6.9 Matrix Enumeration

Chapter 7. Solutions of Linear Systems

7.1 Class Names

7.2 Creating LU Factorizations

7.3 Using LU Factorizations

7.4 Static Methods

Chapter 8. Least Squares

8.1 Class Names

8.2 Creating Least Squares Solutions

8.3 Using Least Squares Solutions

8.4 Nonnegative Least Squares Solutions

Chapter 9. Random Number Generators

9.1 Scalar Random Number Generators

9.2 Vectorized Random Number Generators

Chapter 10. Fourier Transforms, Convolution and Correlation

10.1 Fast Fourier Transforms

10.2 Convolution and Correlation

Chapter 11. Discrete Wavelet Transforms

11.1 Creating Wavelets

11.2 Computing Discrete Wavelet Transforms

Chapter 12. Histograms

12.1 Creating Histograms

12.2 Adding Data to Histograms

12.3 Value Operations of Histograms

12.4 Displaying Histograms

Chapter 13. Calculus

13.1 Encapsulating Functions

13.2 Numerical Integration

13.3 Differentiation

13.4 Polynomials

13.5 Function Interpolation

Chapter 14. Signal Processing

14.1 Moving Window Filtering

14.2 Savitzky-Golay Filtering

14.3 Savitzky-Golay Peak Finding

14.4 Rule-Based Peak Finding

Chapter 15. Special Functions

15.1 Special Functions

Chapter 16. Matrix Functions

Chapter 17. Structured Sparse Matrix Types

17.1 Lower Triangular Matrices

17.2 Upper Triangular Matrices

17.3 Symmetric Matrices

17.4 Hermitian Matrices

17.5 Banded Matrices

17.6 Tridiagonal Matrices

17.7 Symmetric Banded Matrices

17.8 Hermitian Banded Matrices

Chapter 18. Using The Structured Sparse Matrix Classes

18.1 Creating Matrices

18.2 Value Operations on Matrices

18.3 Logical Operations on Matrices

18.4 Arithmetic Operations on Matrices

18.5 Vector Views

18.6 Functions of Matrices

18.7 Generic Functions

Chapter 19. General Sparse Vectors and Matrices

19.1 Sparse Vectors

19.2 Sparse Matrices

19.3 Sparse Matrix Factorizations

Chapter 20. Structured Sparse Matrix Factorizations

20.1 Factorization Classes

20.2 Creating Factorizations

20.3 Using Factorizations

Chapter 21. Least Squares Solutions

21.1 Ordinary Least Squares Methods

21.2 Creating Ordinary Least Squares Objects

21.3 Using Ordinary Least Squares Objects

21.4 Weighted Least Squares

21.5 Iteratively Reweighted Least Squares

Chapter 22. Decompositions

22.1 QR Decompositions

22.2 Singular Value Decompositions

Chapter 23. EigenValue Problems

23.1 Eigenvalue Classnames

23.2 Using the Eigenvalue Classes

23.3 Using the Eigenvalue Server Classes

Chapter 24. The Analysis Namespace

Chapter 25. Encapsulating Multivariate Functions

25.1 Creating Multivariate Functions

25.2 Evaluating Multivariate Functions

25.3 Algebraic Manipulation of Multivariate Functions

Chapter 26. Minimizing Univariate Functions

26.1 Bracketing a Minimum

26.2 Minimizing Functions Without Calculating the Derivative

26.3 Minimizing Derivable Functions

Chapter 27. Minimizing Multivariate Functions

27.1 Minimizing Functions Without Calculating the Derivative

27.2 Minimizing Derivable Functions

Chapter 28. Simulated Annealing

28.1 Temperature

28.2 Annealing Schedules

28.3 Minimizing Functions by Simulated Annealing

28.4 Annealing History

Chapter 29. Linear Programming

29.1 Encapsulating LP Problems

29.2 Solving LP Problems

Chapter 30. Nonlinear and Quadratic Programming

30.1 Objective and Constraint Function Classes

30.2 Nonlinear Programming

30.3 Quadratic Programming

30.4 Constrained Least Squares

Chapter 31. Fitting Polynomials

31.1 Creating PolynomialLeastSquares

31.2 Properties of PolynomialLeastSquares

Chapter 32. Nonlinear Least Squares

32.1 Nonlinear Least Squares Interfaces

32.2 Trust-Region Minimization

32.3 Levenberg-Marquardt Minimization

32.4 Nonlinear Least Squares Curve Fitting

32.5 Nonlinear Least Squares Surface Fitting

Chapter 33. Finding Roots of Univariate Functions

33.1 Finding Function Roots Without Calculating the Derivative

33.2 Finding Function Roots of Derivable Functions

Chapter 34. Integrating Multivariable Functions

34.1 Creating TwoVariableIntegrators

34.2 Integrating Functions of Two Variables

Chapter 35. Differential Equations

35.1 Encapsulating Differential Equations

35.2 Solving Differential Equations

35.3 Dormand–Prince Method

35.4 Stiff Equations

Chapter 36. Statistics Introduction

36.1 Product Features

36.2 Namespaces

Chapter 37. Data Frames

37.1 Column Types

37.2 Creating DataFrames

37.3 Adding and Removing Columns

37.4 Adding and Removing Rows

37.5 Properties of DataFrames

37.6 Accessing DataFrames

37.7 Subsets

37.8 Accessing Sub-Frames

37.9 Reordering DataFrames

37.10 Factors

37.11 Cross-Tabulation

37.12 Exporting Data from DataFrames

Chapter 38. Descriptive Statistics

38.1 Column Types

38.2 Missing Values

38.3 Counts and Sums

38.4 Min/Max Functions

38.5 Ranks, Percentiles, Deciles, and Quartiles

38.6 Central Tendency

38.7 Spread

38.8 Shape

38.9 Covariance, Correlation, and Autocorrelation

38.10 Sorting

38.11 Logical Functions

Chapter 39. Special Functions

39.1 Combinatorial Functions

39.2 Gamma Function

39.3 Beta Function

Chapter 40. Probability Distributions

40.1 Distribution Classes

40.2 Correlated Random Inputs

40.3 Box-Cox Power Transformations

Chapter 41. Hypothesis Tests

41.1 Common Interface

41.2 One Sample Z-Test

41.3 One Sample T-Test

41.4 Two Sample Paired T-Test

41.5 Two Sample Unpaired T-Test

41.6 Two Sample F-Test

41.7 Pearson's Chi-Square Test

41.8 Fisher's Exact Test

Chapter 42. Linear Regression

42.1 Creating Linear Regressions

42.2 Regression Results

42.3 Predictions

42.4 Accessing and Modifying the Model

42.5 Significance of Parameters

42.6 Significance of the Overall Model

Chapter 43. Logistic Regression

43.1 Regression Calculators

43.2 Creating Logistic Regressions

43.3 Checking for Convergence

43.4 Goodness of Fit

43.5 Parameter Estimates

43.6 Predicted Probabilities

43.7 Auxiliary Statistics

Chapter 44. Analysis of Variance

44.1 One-Way ANOVA

44.2 One-Way Repeated Measures ANOVA

44.3 Two-Way Balanced ANOVA

44.4 Two-Way Unbalanced ANOVA

44.5 Two-Way Repeated Measures ANOVA

Chapter 45. Non-Parametric Tests

45.1 One Sample Kolmogorov-Smirnov Test

45.2 Two Sample Kolmogorov-Smirnov Test

45.3 Shapiro-Wilk Test

45.4 One Sample Anderson-Darling Test

45.5 Kruskal-Wallis Test

45.6 Wilcoxon Signed-Rank Test

Chapter 46. Multivariate Techniques

46.1 Principal Component Analysis

46.2 Factor Analysis

46.3 Hierarchical Cluster Analysis

46.4 K-Means Clustering

Chapter 47. Nonnegative Matrix Factorization

47.1 Nonnegative Matrix Factorization

47.2 Data Clustering Using NMF

Chapter 48. Partial Least Squares

48.1 Computing a PLS Regression

48.2 Error Checking

48.3 Predicted Values

48.4 Analysis of Variance

48.5 PLS Algorithms

48.6 Cross Validation

48.7 Partial Least Squares Discriminant Analysis

Chapter 49. Goodness of Fit

49.1 Significance of the Overall Model

49.2 Significance of Parameters

Chapter 50. Process Control

50.1 Process Capability

50.2 Process Performance

50.3 Z Bench

Chapter 51. Serialization

51.1 Binary Serialization

51.2 SOAP Serialization

51.3 XML Serialization

Chapter 52. Database Integration

52.1 Creating ADO.NET Objects from Vectors and Matrices

52.2 Creating Vector and Matrices from ADO.NET Objects

Chapter 53. Error Handling

53.1 Exception Types


Top

Top