OCC.math module¶

class `SwigPyIterator`(*args, **kwargs)

Bases: `object`

`advance`()
`copy`()
`decr`()
`distance`()
`equal`()
`incr`()
`next`()
`previous`()
`thisown`

The membership flag

`value`()
class `math`

Bases: `object`

static `GaussPoints`(*args)
Parameters: Index (int) – Points (math_Vector &) – void
static `GaussPointsMax`(*args)
Return type: int
static `GaussWeights`(*args)
Parameters: Index (int) – Weights (math_Vector &) – void
static `KronrodPointsAndWeights`(*args)
• Returns a vector of Kronrod points and a vector of their weights for Gauss-Kronrod computation method. Index should be odd and greater then or equal to 3, as the number of Kronrod points is equal to 2*N + 1, where N is a number of Gauss points. Points and Weights should have the size equal to Index. Each even element of Points represents a Gauss point value of N-th Gauss quadrature. The values from Index equal to 3 to 123 are stored in a table (see the file math_Kronrod.cxx). If Index is greater, then points and weights will be computed. Returns Standard_True if Index is odd, it is equal to the size of Points and Weights and the computation of Points and Weights is performed successfully. Otherwise this method returns Standard_False.
Parameters: Index (int) – Points (math_Vector &) – Weights (math_Vector &) – bool
static `KronrodPointsMax`(*args)
• Returns the maximal number of points for that the values are stored in the table. If the number is greater then KronrodPointsMax, the points will be computed.
Return type: int
static `OrderedGaussPointsAndWeights`(*args)
• Returns a vector of Gauss points and a vector of their weights. The difference with the method GaussPoints is the following: - the points are returned in increasing order. - if Index is greater then GaussPointsMax, the points are computed. Returns Standard_True if Index is positive, Points’ and Weights’ length is equal to Index, Points and Weights are successfully computed.
Parameters: Index (int) – Points (math_Vector &) – Weights (math_Vector &) – bool
`thisown`

The membership flag

class `math_Array1OfValueAndWeight`(*args)

Bases: `object`

Parameters: Low (int) – Up (int) – Item (math_ValueAndWeight &) – Low – Up – None None
`Assign`()
Parameters: Other (math_Array1OfValueAndWeight &) – math_Array1OfValueAndWeight
`ChangeValue`()
Parameters: Index (int) – math_ValueAndWeight
`Destroy`()
Return type: None
`Init`()
Parameters: V (math_ValueAndWeight &) – None
`IsAllocated`()
Return type: bool
`Length`()
Return type: int
`Lower`()
Return type: int
`Set`()
Parameters: Other (math_Array1OfValueAndWeight &) – math_Array1OfValueAndWeight
`SetValue`()
Parameters: Index (int) – Value (math_ValueAndWeight &) – None
`Upper`()
Return type: int
`Value`()
Parameters: Index (int) – math_ValueAndWeight
`thisown`

The membership flag

class `math_BFGS`(*args)

Bases: `object`

• Given the starting point StartingPoint, the Broyden-Fletcher-Goldfarb-Shanno variant of Davidson-Fletcher-Powell minimization is done on the function F. The tolerance required on F is given by Tolerance. The solution F = Fi is found when : 2.0 * abs(Fi - Fi-1) <= Tolerance * (abs(Fi) + abs(Fi-1) + ZEPS). The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_MultipleVarFunctionWithGradient &) – StartingPoint (math_Vector &) – Tolerance (float) – default value is 1.0e-8 NbIterations (int) – default value is 200 ZEPS (float) – default value is 1.0e-12 None
• Initializes the computation of the minimum of F. Warning A call to the Perform method must be made after this initialization to effectively compute the minimum of the function F.
Parameters: F (math_MultipleVarFunctionWithGradient &) – Tolerance (float) – default value is 1.0e-8 NbIterations (int) – default value is 200 ZEPS (float) – default value is 1.0e-12 None
`Delete`()
Return type: void
`DumpToString`()

math_BFGS_DumpToString(math_BFGS self) -> std::string

`Gradient`()
• Returns the gradient vector at the minimum. Exception NotDone is raised if the minimum was not found.
Return type: math_Vector
• Returns the value of the gradient vector at the minimum in Grad. Exception NotDone is raised if the minimum was not found. Exception DimensionError is raised if the range of Grad is not equal to the range of the StartingPoint.
Parameters: Grad (math_Vector &) – None
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`IsSolutionReached`()
• This method is called at the end of each iteration to check if the solution is found. It can be redefined in a sub-class to implement a specific test to stop the iterations.
Parameters: F (math_MultipleVarFunctionWithGradient &) – bool
`Location`()
• returns the location vector of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: math_Vector
• outputs the location vector of the minimum in Loc. Exception NotDone is raised if the minimum was not found. Exception DimensionError is raised if the range of Loc is not equal to the range of the StartingPoint.
Parameters: Loc (math_Vector &) – None
`Minimum`()
• returns the value of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: float
`NbIterations`()
• Returns the number of iterations really done in the calculation of the minimum. The exception NotDone is raised if the minimum was not found.
Return type: int
`Perform`()
• Is used internally by the constructors.
Parameters: F (math_MultipleVarFunctionWithGradient &) – StartingPoint (math_Vector &) – None
`thisown`

The membership flag

class `math_BissecNewton`(*args)

Bases: `object`

• A combination of Newton-Raphson and bissection methods is done to find the root of the function F between the bounds Bound1 and Bound2. on the function F. The tolerance required on the root is given by TolX. The solution is found when : abs(Xi - Xi-1) <= TolX and F(Xi) * F(Xi-1) <= 0 The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_FunctionWithDerivative &) – Bound1 (float) – Bound2 (float) – TolX (float) – NbIterations (int) – default value is 100 None
`Derivative`()
• returns the value of the derivative at the root. Exception NotDone is raised if the minimum was not found.
Return type: float
`DumpToString`()

math_BissecNewton_DumpToString(math_BissecNewton self) -> std::string

`IsDone`()
• Tests is the root has been successfully found.
Return type: bool
`IsSolutionReached`()
• This method is called at the end of each iteration to check if the solution has been found. It can be redefined in a sub-class to implement a specific test to stop the iterations.
Parameters: F (math_FunctionWithDerivative &) – bool
`Root`()
• returns the value of the root. Exception NotDone is raised if the minimum was not found.
Return type: float
`Value`()
• returns the value of the function at the root. Exception NotDone is raised if the minimum was not found.
Return type: float
`thisown`

The membership flag

class `math_BracketMinimum`(*args)

Bases: `object`

• Given two initial values this class computes a bracketing triplet of abscissae Ax, Bx, Cx (such that Bx is between Ax and Cx, F(Bx) is less than both F(Bx) and F(Cx)) the Brent minimization is done on the function F.
Parameters: F (math_Function &) – A (float) – B (float) – None
• Given two initial values this class computes a bracketing triplet of abscissae Ax, Bx, Cx (such that Bx is between Ax and Cx, F(Bx) is less than both F(Bx) and F(Cx)) the Brent minimization is done on the function F. This constructor has to be used if F(A) is known.
Parameters: F (math_Function &) – A (float) – B (float) – FA (float) – None
• Given two initial values this class computes a bracketing triplet of abscissae Ax, Bx, Cx (such that Bx is between Ax and Cx, F(Bx) is less than both F(Bx) and F(Cx)) the Brent minimization is done on the function F. This constructor has to be used if F(A) and F(B) are known.
Parameters: F (math_Function &) – A (float) – B (float) – FA (float) – FB (float) – None
`DumpToString`()

math_BracketMinimum_DumpToString(math_BracketMinimum self) -> std::string

`FunctionValues`()
• returns the bracketed triplet function values. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false).
Parameters: FA (float &) – FB (float &) – FC (float &) – None
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`Values`()
• Returns the bracketed triplet of abscissae. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false).
Parameters: A (float &) – B (float &) – C (float &) – None
`thisown`

The membership flag

class `math_BracketedRoot`(*args)

Bases: `object`

• The Brent method is used to find the root of the function F between the bounds Bound1 and Bound2 on the function F. If F(Bound1)*F(Bound2) >0 the Brent method fails. The tolerance required for the root is given by Tolerance. The solution is found when : abs(Xi - Xi-1) <= Tolerance; The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_Function &) – Bound1 (float) – Bound2 (float) – Tolerance (float) – NbIterations (int) – default value is 100 ZEPS (float) – default value is 1.0e-12 None
`DumpToString`()

math_BracketedRoot_DumpToString(math_BracketedRoot self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`NbIterations`()
• returns the number of iterations really done during the computation of the Root. Exception NotDone is raised if the minimum was not found.
Return type: int
`Root`()
• returns the value of the root. Exception NotDone is raised if the minimum was not found.
Return type: float
`Value`()
• returns the value of the function at the root. Exception NotDone is raised if the minimum was not found.
Return type: float
`thisown`

The membership flag

class `math_BrentMinimum`(*args)

Bases: `object`

• This constructor should be used in a sub-class to initialize correctly all the fields of this class.
Parameters: TolX (float) – NbIterations (int) – default value is 100 ZEPS (float) – default value is 1.0e-12 None
• This constructor should be used in a sub-class to initialize correctly all the fields of this class. It has to be used if F(Bx) is known.
Parameters: TolX (float) – Fbx (float) – NbIterations (int) – default value is 100 ZEPS (float) – default value is 1.0e-12 None
• Given a bracketing triplet of abscissae Ax, Bx, Cx (such as Bx is between Ax and Cx, F(Bx) is less than both F(Bx) and F(Cx)) the Brent minimization is done on the function F. The tolerance required on F is given by Tolerance. The solution is found when : abs(Xi - Xi-1) <= TolX * abs(Xi) + ZEPS; The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_Function &) – Ax (float) – Bx (float) – Cx (float) – TolX (float) – NbIterations (int) – default value is 100 ZEPS (float) – default value is 1.0e-12 None
`DumpToString`()

math_BrentMinimum_DumpToString(math_BrentMinimum self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`IsSolutionReached`()
• This method is called at the end of each iteration to check if the solution is found. It can be redefined in a sub-class to implement a specific test to stop the iterations.
Parameters: F (math_Function &) – bool
`Location`()
• returns the location value of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: float
`Minimum`()
• returns the value of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: float
`NbIterations`()
• returns the number of iterations really done during the computation of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: int
`Perform`()
• Brent minimization is performed on function F from a given bracketing triplet of abscissas Ax, Bx, Cx (such that Bx is between Ax and Cx, F(Bx) is less than both F(Bx) and F(Cx)) Warning The initialization constructors must have been called before the call to the Perform method.
Parameters: F (math_Function &) – Ax (float) – Bx (float) – Cx (float) – None
`thisown`

The membership flag

class `math_BullardGenerator`(*args)

Bases: `object`

• Creates new Xorshift 64-bit RNG.
Parameters: theSeed (unsigned int) – default value is 1 None
`NextInt`()
• Generates new 64-bit integer value.
Return type: unsigned int
`NextReal`()
• Generates new floating-point value.
Return type: float
`thisown`

The membership flag

class `math_CompareOfValueAndWeight`(*args)

Bases: `object`

Return type: None
`IsEqual`()
• returns True when <Right> and <Left> are equal.
Parameters: Left (math_ValueAndWeight &) – Right (math_ValueAndWeight &) – bool
`IsGreater`()
• Returns True if <Left.Value()> is greater than <Right.Value()>.
Parameters: Left (math_ValueAndWeight &) – Right (math_ValueAndWeight &) – bool
`IsLower`()
• Returns True if <Left.Value()> is lower than <Right.Value()>.
Parameters: Left (math_ValueAndWeight &) – Right (math_ValueAndWeight &) – bool
`thisown`

The membership flag

class `math_ComputeGaussPointsAndWeights`(*args)

Bases: `object`

Parameters: Number (int) – None
`IsDone`()
Return type: bool
`Points`()
Return type: math_Vector
`Weights`()
Return type: math_Vector
`thisown`

The membership flag

class `math_ComputeKronrodPointsAndWeights`(*args)

Bases: `object`

Parameters: Number (int) – None
`IsDone`()
Return type: bool
`Points`()
Return type: math_Vector
`Weights`()
Return type: math_Vector
`thisown`

The membership flag

class `math_Crout`(*args)

Bases: `object`

• Given an input matrix A, this algorithm inverts A by the Crout algorithm. The user can give only the inferior triangle for the implementation. A can be decomposed like this: A = L * D * T(L) where L is triangular inferior and D is diagonal. If one element of A is less than MinPivot, A is considered as singular. Exception NotSquare is raised if A is not a square matrix.
Parameters: A (math_Matrix &) – MinPivot (float) – default value is 1.0e-20 None
`Determinant`()
• Returns the value of the determinant of the previously LU decomposed matrix A. Zero is returned if the matrix A is considered as singular. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false).
Return type: float
`DumpToString`()

math_Crout_DumpToString(math_Crout self) -> std::string

`Inverse`()
• returns the inverse matrix of A. Only the inferior triangle is returned. Exception NotDone is raised if NotDone.
Return type: math_Matrix
`Invert`()
• returns in Inv the inverse matrix of A. Only the inferior triangle is returned. Exception NotDone is raised if NotDone.
Parameters: Inv (math_Matrix &) – None
`IsDone`()
• Returns True if all has been correctly done.
Return type: bool
`Solve`()
• Given an input vector <B>, this routine returns the solution of the set of linear equations A . X = B. Exception NotDone is raised if the decomposition was not done successfully. Exception DimensionError is raised if the range of B is not equal to the rowrange of A.
Parameters: B (math_Vector &) – X (math_Vector &) – None
`thisown`

The membership flag

class `math_DirectPolynomialRoots`(*args)

Bases: `object`

• computes all the real roots of the polynomial Ax4 + Bx3 + Cx2 + Dx + E using a direct method.
Parameters: A (float) – B (float) – C (float) – D (float) – E (float) – None
• computes all the real roots of the polynomial Ax3 + Bx2 + Cx + D using a direct method.
Parameters: A (float) – B (float) – C (float) – D (float) – None
• computes all the real roots of the polynomial Ax2 + Bx + C using a direct method.
Parameters: A (float) – B (float) – C (float) – None
• computes the real root of the polynomial Ax + B.
Parameters: A (float) – B (float) – None
`DumpToString`()

math_DirectPolynomialRoots_DumpToString(math_DirectPolynomialRoots self) -> std::string

`InfiniteRoots`()
• Returns true if there is an infinity of roots, otherwise returns false.
Return type: bool
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`NbSolutions`()
• returns the number of solutions. An exception is raised if there are an infinity of roots.
Return type: int
`Value`()
• returns the value of the Nieme root. An exception is raised if there are an infinity of roots. Exception RangeError is raised if Nieme is < 1 or Nieme > NbSolutions.
Parameters: Nieme (int) – float
`thisown`

The membership flag

class `math_DoubleTab`(*args)

Bases: `object`

Parameters: LowerRow (int) – UpperRow (int) – LowerCol (int) – UpperCol (int) – Tab (Standard_Address) – LowerRow – UpperRow – LowerCol – UpperCol – Other (math_DoubleTab &) – None None None
`Copy`()
Parameters: Other (math_DoubleTab &) – None
`Free`()
Return type: None
`Init`()
Parameters: InitValue (float) – None
`SetLowerCol`()
Parameters: LowerCol (int) – None
`SetLowerRow`()
Parameters: LowerRow (int) – None
`Value`()
Parameters: RowIndex (int) – ColIndex (int) – float
`thisown`

The membership flag

class `math_EigenValuesSearcher`(*args)

Bases: `object`

Parameters: Diagonal (TColStd_Array1OfReal &) – Subdiagonal (TColStd_Array1OfReal &) – None
`Dimension`()
• Returns the dimension of matrix
Return type: int
`EigenValue`()
• Returns the Index_th eigen value of matrix Index must be in [1, Dimension()]
Parameters: Index (int) – float
`EigenVector`()
• Returns the Index_th eigen vector of matrix Index must be in [1, Dimension()]
Parameters: Index (int) – math_Vector
`IsDone`()
• Returns Standard_True if computation is performed successfully.
Return type: bool
`thisown`

The membership flag

class `math_FRPR`(*args)

Bases: `object`

• Computes FRPR minimization function F from input vector StartingPoint. The solution F = Fi is found when 2.0 * abs(Fi - Fi-1) <= Tolerance * (abs(Fi) + abs(Fi-1) + ZEPS). The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_MultipleVarFunctionWithGradient &) – StartingPoint (math_Vector &) – Tolerance (float) – NbIterations (int) – default value is 200 ZEPS (float) – default value is 1.0e-12 None
• Purpose Initializes the computation of the minimum of F. Warning A call to the Perform method must be made after this initialization to compute the minimum of the function.
Parameters: F (math_MultipleVarFunctionWithGradient &) – Tolerance (float) – NbIterations (int) – default value is 200 ZEPS (float) – default value is 1.0e-12 None
`Delete`()
Return type: void
`DumpToString`()

math_FRPR_DumpToString(math_FRPR self) -> std::string

`Gradient`()
• returns the gradient vector at the minimum. Exception NotDone is raised if the minimum was not found.
Return type: math_Vector
• outputs the gradient vector at the minimum in Grad. Exception NotDone is raised if the minimum was not found. Exception DimensionError is raised if the range of Grad is not equal to the range of the StartingPoint.
Parameters: Grad (math_Vector &) – None
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`IsSolutionReached`()
• The solution F = Fi is found when : 2.0 * abs(Fi - Fi-1) <= Tolerance * (abs(Fi) + abs(Fi-1)) + ZEPS. The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_MultipleVarFunctionWithGradient &) – bool
`Location`()
• returns the location vector of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: math_Vector
• outputs the location vector of the minimum in Loc. Exception NotDone is raised if the minimum was not found. Exception DimensionError is raised if the range of Loc is not equal to the range of the StartingPoint.
Parameters: Loc (math_Vector &) – None
`Minimum`()
• returns the value of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: float
`NbIterations`()
• returns the number of iterations really done during the computation of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: int
`Perform`()
• Use this method after a call to the initialization constructor to compute the minimum of function F. Warning The initialization constructor must have been called before the Perform method is called
Parameters: F (math_MultipleVarFunctionWithGradient &) – StartingPoint (math_Vector &) – None
`thisown`

The membership flag

class `math_Function`(*args, **kwargs)

Bases: `object`

`GetStateNumber`()
• returns the state of the function corresponding to the latest call of any methods associated with the function. This function is called by each of the algorithms described later which defined the function Integer Algorithm::StateNumber(). The algorithm has the responsibility to call this function when it has found a solution (i.e. a root or a minimum) and has to maintain the association between the solution found and this StateNumber. Byu default, this method returns 0 (which means for the algorithm: no state has been saved). It is the responsibility of the programmer to decide if he needs to save the current state of the function and to return an Integer that allows retrieval of the state.
Return type: int
`Value`()
• Computes the value of the function <F> for a given value of variable <X>. returns True if the computation was done successfully, False otherwise.
Parameters: X (float) – F (float &) – bool
`thisown`

The membership flag

class `math_FunctionAllRoots`(*args)

Bases: `object`

• The algorithm uses the sample to find intervals on which the function is null. An interval is found if, for at least two consecutive points of the sample, Ui and Ui+1, we get |F(Ui)|<=EpsNul and |F(Ui+1)|<=EpsNul. The real bounds of an interval are computed with the FunctionRoots. algorithm. Between two intervals, the roots of the function F are calculated using the FunctionRoots algorithm.
Parameters: F (math_FunctionWithDerivative &) – S (math_FunctionSample &) – EpsX (float) – EpsF (float) – EpsNul (float) – None
`DumpToString`()

math_FunctionAllRoots_DumpToString(math_FunctionAllRoots self) -> std::string

`GetInterval`()
• Returns the interval of parameter of range Index. An exception is raised if IsDone returns False; An exception is raised if Index<=0 or Index >Nbintervals.
Parameters: Index (int) – A (float &) – B (float &) – None
`GetIntervalState`()
• returns the State Number associated to the interval Index. An exception is raised if IsDone returns False; An exception is raised if Index<=0 or Index >Nbintervals.
Parameters: Index (int) – IFirst (int &) – ILast (int &) – None
`GetPoint`()
• Returns the parameter of the point of range Index. An exception is raised if IsDone returns False; An exception is raised if Index<=0 or Index >NbPoints.
Parameters: Index (int) – float
`GetPointState`()
• returns the State Number associated to the point Index. An exception is raised if IsDone returns False; An exception is raised if Index<=0 or Index >Nbintervals.
Parameters: Index (int) – int
`IsDone`()
• Returns True if the computation has been done successfully.
Return type: bool
`NbIntervals`()
• Returns the number of intervals on which the function is Null. An exception is raised if IsDone returns False.
Return type: int
`NbPoints`()
• returns the number of points where the function is Null. An exception is raised if IsDone returns False.
Return type: int
`thisown`

The membership flag

class `math_FunctionRoot`(*args)

Bases: `object`

• The Newton-Raphson method is done to find the root of the function F from the initial guess Guess.The tolerance required on the root is given by Tolerance. Iterations are stopped if the expected solution does not stay in the range A..B. The solution is found when abs(Xi - Xi-1) <= Tolerance; The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_FunctionWithDerivative &) – Guess (float) – Tolerance (float) – NbIterations (int) – default value is 100 None
• The Newton-Raphson method is done to find the root of the function F from the initial guess Guess. The tolerance required on the root is given by Tolerance. Iterations are stopped if the expected solution does not stay in the range A..B The solution is found when abs(Xi - Xi-1) <= Tolerance; The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_FunctionWithDerivative &) – Guess (float) – Tolerance (float) – A (float) – B (float) – NbIterations (int) – default value is 100 None
`Derivative`()
• returns the value of the derivative at the root. Exception NotDone is raised if the root was not found.
Return type: float
`DumpToString`()

math_FunctionRoot_DumpToString(math_FunctionRoot self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`NbIterations`()
• returns the number of iterations really done on the computation of the Root. Exception NotDone is raised if the root was not found.
Return type: int
`Root`()
• returns the value of the root. Exception NotDone is raised if the root was not found.
Return type: float
`Value`()
• returns the value of the function at the root. Exception NotDone is raised if the root was not found.
Return type: float
`thisown`

The membership flag

class `math_FunctionRoots`(*args)

Bases: `object`

• Calculates all the real roots of a function F-K within the range A..B. whithout conditions on A and B A solution X is found when abs(Xi - Xi-1) <= Epsx and abs(F(Xi)-K) <= EpsF. The function is considered as null between A and B if abs(F-K) <= EpsNull within this range.
Parameters: F (math_FunctionWithDerivative &) – A (float) – B (float) – NbSample (int) – EpsX (float) – default value is 0.0 EpsF (float) – default value is 0.0 EpsNull (float) – default value is 0.0 K (float) – default value is 0.0 None
`DumpToString`()

math_FunctionRoots_DumpToString(math_FunctionRoots self) -> std::string

`IsAllNull`()
• returns true if the function is considered as null between A and B. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false).
Return type: bool
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`NbSolutions`()
• Returns the number of solutions found. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false).
Return type: int
`StateNumber`()
• returns the StateNumber of the Nieme root. Exception RangeError is raised if Nieme is < 1 or Nieme > NbSolutions.
Parameters: Nieme (int) – int
`Value`()
• Returns the Nth value of the root of function F. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false).
Parameters: Nieme (int) – float
`thisown`

The membership flag

class `math_FunctionSample`(*args)

Bases: `object`

Parameters: A (float) – B (float) – N (int) – None
`Bounds`()
• Returns the bounds of parameters.
Parameters: A (float &) – B (float &) – void
`GetParameter`()
• Returns the value of parameter of the point of range Index : A + ((Index-1)/(NbPoints-1))*B. An exception is raised if Index<=0 or Index>NbPoints.
Parameters: Index (int) – float
`NbPoints`()
• Returns the number of sample points.
Return type: int
`thisown`

The membership flag

class `math_FunctionSet`(*args, **kwargs)

Bases: `object`

`Delete`()
Return type: void
`GetStateNumber`()
• Returns the state of the function corresponding to the latestcall of any methods associated with the function. This function is called by each of the algorithms described later which define the function Integer Algorithm::StateNumber(). The algorithm has the responsibility to call this function when it has found a solution (i.e. a root or a minimum) and has to maintain the association between the solution found and this StateNumber. Byu default, this method returns 0 (which means for the algorithm: no state has been saved). It is the responsibility of the programmer to decide if he needs to save the current state of the function and to return an Integer that allows retrieval of the state.
Return type: int
`NbEquations`()
• Returns the number of equations of the function.
Return type: int
`NbVariables`()
• Returns the number of variables of the function.
Return type: int
`Value`()
• Computes the values <F> of the functions for the variable <X>. returns True if the computation was done successfully, False otherwise.
Parameters: X (math_Vector &) – F (math_Vector &) – bool
`thisown`

The membership flag

class `math_FunctionSetRoot`(*args)

Bases: `object`

• is used in a sub-class to initialize correctly all the fields of this class. The range (1, F.NbVariables()) must be especially respected for all vectors and matrix declarations.
Parameters: F (math_FunctionSetWithDerivatives &) – Tolerance (math_Vector &) – NbIterations (int) – default value is 100 None
• is used in a sub-class to initialize correctly all the fields of this class. The range (1, F.NbVariables()) must be especially respected for all vectors and matrix declarations. The method SetTolerance must be called after this constructor.
Parameters: F (math_FunctionSetWithDerivatives &) – NbIterations (int) – default value is 100 None
• is used to improve the root of the function F from the initial guess StartingPoint. The maximum number of iterations allowed is given by NbIterations. In this case, the solution is found when: abs(Xi - Xi-1)(j) <= Tolerance(j) for all unknowns.
Parameters: F (math_FunctionSetWithDerivatives &) – StartingPoint (math_Vector &) – Tolerance (math_Vector &) – NbIterations (int) – default value is 100 None
• is used to improve the root of the function F from the initial guess StartingPoint. The maximum number of iterations allowed is given by NbIterations. In this case, the solution is found when: abs(Xi - Xi-1) <= Tolerance for all unknowns.
Parameters: F (math_FunctionSetWithDerivatives &) – StartingPoint (math_Vector &) – Tolerance (math_Vector &) – infBound (math_Vector &) – supBound (math_Vector &) – NbIterations (int) – default value is 100 theStopOnDivergent (bool) – default value is Standard_False None
`Delete`()
Return type: void
`Derivative`()
• Returns the matrix value of the derivative at the root. Exception NotDone is raised if the root was not found.
Return type: math_Matrix
• outputs the matrix value of the derivative at the root in Der. Exception NotDone is raised if the root was not found. Exception DimensionError is raised if the column range of <Der> is not equal to the range of the startingPoint.
Parameters: Der (math_Matrix &) – None
`DumpToString`()

math_FunctionSetRoot_DumpToString(math_FunctionSetRoot self) -> std::string

`FunctionSetErrors`()
• returns the vector value of the error done on the functions at the root. Exception NotDone is raised if the root was not found.
Return type: math_Vector
• outputs the vector value of the error done on the functions at the root in Err. Exception NotDone is raised if the root was not found. Exception DimensionError is raised if the range of Err is not equal to the range of the StartingPoint.
Parameters: Err (math_Vector &) – None
`IsDivergent`()
Return type: bool
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`IsSolutionReached`()
• This routine is called at the end of each iteration to check if the solution was found. It can be redefined in a sub-class to implement a specific test to stop the iterations. In this case, the solution is found when: abs(Xi - Xi-1) <= Tolerance for all unknowns.
Parameters: F (math_FunctionSetWithDerivatives &) – bool
`NbIterations`()
• Returns the number of iterations really done during the computation of the root. Exception NotDone is raised if the root was not found.
Return type: int
`Perform`()
• Improves the root of function F from the initial guess StartingPoint. infBound and supBound may be given to constrain the solution. Warning This method is called when computation of the solution is not performed by the constructors.
Parameters: F (math_FunctionSetWithDerivatives &) – StartingPoint (math_Vector &) – infBound (math_Vector &) – supBound (math_Vector &) – theStopOnDivergent (bool) – default value is Standard_False None
`Root`()
• Returns the value of the root of function F. Exception NotDone is raised if the root was not found.
Return type: math_Vector
• Outputs the root vector in Root. Exception NotDone is raised if the root was not found. Exception DimensionError is raised if the range of Root is not equal to the range of the StartingPoint.
Parameters: Root (math_Vector &) – None
`SetTolerance`()
• Initializes the tolerance values.
Parameters: Tolerance (math_Vector &) – None
`StateNumber`()
• returns the stateNumber (as returned by F.GetStateNumber()) associated to the root found.
Return type: int
`thisown`

The membership flag

class `math_FunctionSetWithDerivatives`(*args, **kwargs)
`Derivatives`()
• Returns the values <D> of the derivatives for the variable <X>. Returns True if the computation was done successfully, False otherwise.
Parameters: X (math_Vector &) – D (math_Matrix &) – bool
`Values`()
• returns the values <F> of the functions and the derivatives <D> for the variable <X>. Returns True if the computation was done successfully, False otherwise.
Parameters: X (math_Vector &) – F (math_Vector &) – D (math_Matrix &) – bool
`thisown`

The membership flag

class `math_FunctionWithDerivative`(*args, **kwargs)
`Delete`()
Return type: void
`Derivative`()
• Computes the derivative <D> of the function for the variable <X>. Returns True if the calculation were successfully done, False otherwise.
Parameters: X (float) – D (float &) – bool
`Values`()
• Computes the value <F> and the derivative <D> of the function for the variable <X>. Returns True if the calculation were successfully done, False otherwise.
Parameters: X (float) – F (float &) – D (float &) – bool
`thisown`

The membership flag

class `math_Gauss`(*args)

Bases: `object`

• Given an input n X n matrix A this constructor performs its LU decomposition with partial pivoting (interchange of rows). This LU decomposition is stored internally and may be used to do subsequent calculation. If the largest pivot found is less than MinPivot the matrix A is considered as singular. Exception NotSquare is raised if A is not a square matrix.
Parameters: A (math_Matrix &) – MinPivot (float) – default value is 1.0e-20 None
`Determinant`()
• This routine returns the value of the determinant of the previously LU decomposed matrix A. Exception NotDone may be raised if the decomposition of A was not done successfully, zero is returned if the matrix A was considered as singular.
Return type: float
`DumpToString`()

math_Gauss_DumpToString(math_Gauss self) -> std::string

`Invert`()
• This routine outputs Inv the inverse of the previously LU decomposed matrix A. Exception DimensionError is raised if the ranges of B are not equal to the ranges of A.
Parameters: Inv (math_Matrix &) – None
`IsDone`()
• Returns true if the computations are successful, otherwise returns false
Return type: bool
`Solve`()
• Given the input Vector B this routine returns the solution X of the set of linear equations A . X = B. Exception NotDone is raised if the decomposition of A was not done successfully. Exception DimensionError is raised if the range of B is not equal to the number of rows of A.
Parameters: B (math_Vector &) – X (math_Vector &) – None
• Given the input Vector B this routine solves the set of linear equations A . X = B. B is replaced by the vector solution X. Exception NotDone is raised if the decomposition of A was not done successfully. Exception DimensionError is raised if the range of B is not equal to the number of rows of A.
Parameters: B (math_Vector &) – None
`thisown`

The membership flag

class `math_GaussLeastSquare`(*args)

Bases: `object`

• Given an input n X m matrix A with n >= m this constructor performs the LU decomposition with partial pivoting (interchange of rows) of the matrix AA = A.Transposed() * A; This LU decomposition is stored internally and may be used to do subsequent calculation. If the largest pivot found is less than MinPivot the matrix <A> is considered as singular.
Parameters: A (math_Matrix &) – MinPivot (float) – default value is 1.0e-20 None
`DumpToString`()

math_GaussLeastSquare_DumpToString(math_GaussLeastSquare self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.e
Return type: bool
`Solve`()
• Given the input Vector <B> this routine solves the set of linear equations A . X = B. Exception NotDone is raised if the decomposition of A was not done successfully. Exception DimensionError is raised if the range of B Inv is not equal to the rowrange of A. Exception DimensionError is raised if the range of X Inv is not equal to the colrange of A.
Parameters: B (math_Vector &) – X (math_Vector &) – None
`thisown`

The membership flag

class `math_GaussMultipleIntegration`(*args)

Bases: `object`

• The Gauss-Legendre integration with Order = points of integration for each unknow, is done on the function F between the bounds Lower and Upper.
Parameters: F (math_MultipleVarFunction &) – Lower (math_Vector &) – Upper (math_Vector &) – Order (math_IntegerVector &) – None
`DumpToString`()

math_GaussMultipleIntegration_DumpToString(math_GaussMultipleIntegration self) -> std::string

`IsDone`()
• returns True if all has been correctly done.
Return type: bool
`Value`()
• returns the value of the integral.
Return type: float
`thisown`

The membership flag

`math_GaussPoints`(*args)
Parameters: Index (int) – Points (math_Vector &) – void
`math_GaussPointsMax`(*args)
Return type: int
class `math_GaussSetIntegration`(*args)

Bases: `object`

• The Gauss-Legendre integration with Order = points of integration for each unknow, is done on the function F between the bounds Lower and Upper.
Parameters: F (math_FunctionSet &) – Lower (math_Vector &) – Upper (math_Vector &) – Order (math_IntegerVector &) – None
`DumpToString`()

math_GaussSetIntegration_DumpToString(math_GaussSetIntegration self) -> std::string

`IsDone`()
• returns True if all has been correctly done.
Return type: bool
`Value`()
• returns the value of the integral.
Return type: math_Vector
`thisown`

The membership flag

class `math_GaussSingleIntegration`(*args)

Bases: `object`

Return type: None
• The Gauss-Legendre integration with N = Order points of integration, is done on the function F between the bounds Lower and Upper.
Parameters: F (math_Function &) – Lower (float) – Upper (float) – Order (int) – None
• The Gauss-Legendre integration with N = Order points of integration and given tolerance = Tol is done on the function F between the bounds Lower and Upper.
Parameters: F (math_Function &) – Lower (float) – Upper (float) – Order (int) – Tol (float) – None
`DumpToString`()

math_GaussSingleIntegration_DumpToString(math_GaussSingleIntegration self) -> std::string

`IsDone`()
• returns True if all has been correctly done.
Return type: bool
`Value`()
• returns the value of the integral.
Return type: float
`thisown`

The membership flag

`math_GaussWeights`(*args)
Parameters: Index (int) – Weights (math_Vector &) – void
class `math_GlobOptMin`(*args)

Bases: `object`

Parameters: theFunc (math_MultipleVarFunction *) – theA (math_Vector &) – theB (math_Vector &) – theC (float) – default value is 9 theDiscretizationTol (float) – default value is 1.0e-2 theSameTol (float) – default value is 1.0e-7 None
`GetF`()
• Get best functional value.
Return type: float
`GetTol`()
Parameters: theDiscretizationTol (float &) – theSameTol (float &) – None
`NbExtrema`()
• Return count of global extremas.
Return type: int
`Perform`()
Return type: None
`Points`()
• Return solution i, 1 <= i <= NbExtrema.
Parameters: theIndex (int) – theSol (math_Vector &) – None
`SetGlobalParams`()
Parameters: theFunc (math_MultipleVarFunction *) – theA (math_Vector &) – theB (math_Vector &) – theC (float) – default value is 9 theDiscretizationTol (float) – default value is 1.0e-2 theSameTol (float) – default value is 1.0e-7 None
`SetLocalParams`()
Parameters: theLocalA (math_Vector &) – theLocalB (math_Vector &) – None
`SetTol`()
Parameters: theDiscretizationTol (float) – theSameTol (float) – None
`thisown`

The membership flag

class `math_Householder`(*args)

Bases: `object`

• Given an input matrix A with n>= m, given an input matrix B this constructor performs the least square resolution of the set of linear equations A.X = B for each column of B. If a column norm is less than EPS, the resolution can’t be done. Exception DimensionError is raised if the row number of B is different from the A row number.
Parameters: A (math_Matrix &) – B (math_Matrix &) – EPS (float) – default value is 1.0e-20 None
• Given an input matrix A with n>= m, given an input matrix B this constructor performs the least square resolution of the set of linear equations A.X = B for each column of B. If a column norm is less than EPS, the resolution can’t be done. Exception DimensionError is raised if the row number of B is different from the A row number.
Parameters: A (math_Matrix &) – B (math_Matrix &) – lowerArow (int) – upperArow (int) – lowerAcol (int) – upperAcol (int) – EPS (float) – default value is 1.0e-20 None
• Given an input matrix A with n>= m, given an input vector B this constructor performs the least square resolution of the set of linear equations A.X = B. If a column norm is less than EPS, the resolution can’t be done. Exception DimensionError is raised if the length of B is different from the A row number.
Parameters: A (math_Matrix &) – B (math_Vector &) – EPS (float) – default value is 1.0e-20 None
`AllValues`()
• Returns the matrix sol of all the solutions of the system A.X = B. Exception NotDone is raised is the resolution has not be done.
Return type: math_Matrix
`DumpToString`()

math_Householder_DumpToString(math_Householder self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`Value`()
• Given the integer Index, this routine returns the corresponding least square solution sol. Exception NotDone is raised if the resolution has not be done. Exception OutOfRange is raised if Index <=0 or Index is more than the number of columns of B.
Parameters: sol (math_Vector &) – Index (int) – default value is 1 None
`thisown`

The membership flag

class `math_IntegerRandom`(*args)

Bases: `object`

• creates a Integer random generator between the values Lower and Upper.
Parameters: Lower (int) – Upper (int) – None
`Next`()
• returns the next random number.
Return type: int
`Reset`()
• reinitializes the random generator
Return type: None
`thisown`

The membership flag

class `math_IntegerVector`(*args)

Bases: `object`

• contructs an IntegerVector in the range [Lower..Upper]
Parameters: theFirst (int) – theLast (int) – None
• contructs an IntegerVector in the range [Lower..Upper] with all the elements set to theInitialValue.
Parameters: theFirst (int) – theLast (int) – theInitialValue (int) – None
• constructs an IntegerVector in the range [Lower..Upper] which share the ‘c array’ theTab.
Parameters: theTab (Standard_Address) – theFirst (int) – theLast (int) – None
• constructs a copy for initialization. An exception is raised if the lengths of the IntegerVectors are different.
Parameters: theOther (math_IntegerVector &) – None
`Add`()
• adds the IntegerVector ‘theRight’ to an IntegerVector. An exception is raised if the IntegerVectors have not the same length. An exception is raised if the lengths are not equal.
Parameters: theRight (math_IntegerVector &) – None
• sets an IntegerVector to the sum of the IntegerVector ‘theLeft’ and the IntegerVector ‘theRight’. An exception is raised if the lengths are different.
Parameters: theLeft (math_IntegerVector &) – theRight (math_IntegerVector &) – None
`Added`()
• adds the IntegerVector ‘theRight’ to an IntegerVector. An exception is raised if the IntegerVectors have not the same length. An exception is raised if the lengths are not equal.
Parameters: theRight (math_IntegerVector &) – math_IntegerVector
`DumpToString`()

math_IntegerVector_DumpToString(math_IntegerVector self) -> std::string

`Init`()
• Initialize an IntegerVector with all the elements set to theInitialValue.
Parameters: theInitialValue (int) – None
`Initialized`()
• Initialises an IntegerVector by copying ‘theOther’. An exception is raised if the Lengths are different.
Parameters: theOther (math_IntegerVector &) – math_IntegerVector
`Inverse`()
• returns the inverse IntegerVector of an IntegerVector.
Return type: math_IntegerVector
`Invert`()
• inverses an IntegerVector.
Return type: None
`Length`()
• returns the length of an IntegerVector
Return type: inline int
`Lower`()
• returns the value of the Lower index of an IntegerVector.
Return type: inline int
`Max`()
• returns the value of the Index of the maximum element of an IntegerVector.
Return type: int
`Min`()
• returns the value of the Index of the minimum element of an IntegerVector.
Return type: int
`Multiplied`()
• returns the product of an IntegerVector by an integer value.
Parameters: theRight (int) – math_IntegerVector
• returns the inner product of 2 IntegerVectors. An exception is raised if the lengths are not equal.
Parameters: theRight (math_IntegerVector &) – int
`Multiply`()
• returns the product of an IntegerVector by an integer value.
Parameters: theRight (int) – None
• returns the multiplication of an integer by an IntegerVector.
Parameters: theLeft (int) – theRight (math_IntegerVector &) – None
`Norm`()
• returns the value of the norm of an IntegerVector.
Return type: float
`Norm2`()
• returns the value of the square of the norm of an IntegerVector.
Return type: float
`Opposite`()
• returns the opposite of an IntegerVector.
Return type: math_IntegerVector
`Set`()
• sets an IntegerVector from ‘theI1’ to ‘theI2’ to the IntegerVector ‘theV’; An exception is raised if ‘theI1’ is less than ‘LowerIndex’ or ‘theI2’ is greater than ‘UpperIndex’ or ‘theI1’ is greater than ‘theI2’. An exception is raised if ‘theI2-theI1+1’ is different from the Length of ‘theV’.
Parameters: theI1 (int) – theI2 (int) – theV (math_IntegerVector &) – theOther (math_IntegerVector &) – None math_IntegerVector
`Slice`()
• slices the values of the IntegerVector between ‘theI1’ and ‘theI2’: Example: [2, 1, 2, 3, 4, 5] becomes [2, 4, 3, 2, 1, 5] between 2 and 5. An exception is raised if ‘theI1’ is less than ‘LowerIndex’ or ‘theI2’ is greater than ‘UpperIndex’.
Parameters: theI1 (int) – theI2 (int) – math_IntegerVector
`Subtract`()
• sets an IntegerVector to the substraction of ‘theRight’ from ‘theLeft’. An exception is raised if the IntegerVectors have not the same length.
Parameters: theLeft (math_IntegerVector &) – theRight (math_IntegerVector &) – None
• returns the subtraction of ‘theRight’ from ‘me’. An exception is raised if the IntegerVectors have not the same length.
Parameters: theRight (math_IntegerVector &) – None
`Subtracted`()
• returns the subtraction of ‘theRight’ from ‘me’. An exception is raised if the IntegerVectors have not the same length.
Parameters: theRight (math_IntegerVector &) – math_IntegerVector
`TMultiplied`()
• returns the product of a vector and a real value.
Parameters: theRight (int) – math_IntegerVector
`Upper`()
• returns the value of the Upper index of an IntegerVector.
Return type: inline int
`Value`()
• accesses (in read or write mode) the value of index theNum of an IntegerVector.
Parameters: theNum (int) – inline int
`thisown`

The membership flag

class `math_Jacobi`(*args)

Bases: `object`

• Given a Real n X n matrix A, this constructor computes all its eigenvalues and eigenvectors using the Jacobi method. The exception NotSquare is raised if the matrix is not square. No verification that the matrix A is really symmetric is done.
Parameters: A (math_Matrix &) – None
`DumpToString`()

math_Jacobi_DumpToString(math_Jacobi self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`Value`()
• returns the eigenvalue number Num. Eigenvalues are in the range (1..n). Exception NotDone is raised if calculation is not done successfully.
Parameters: Num (int) – float
`Values`()
• Returns the eigenvalues vector. Exception NotDone is raised if calculation is not done successfully.
Return type: math_Vector
`Vector`()
• Returns the eigenvector V of number Num. Eigenvectors are in the range (1..n). Exception NotDone is raised if calculation is not done successfully.
Parameters: Num (int) – V (math_Vector &) – None
`Vectors`()
• returns the eigenvectors matrix. Exception NotDone is raised if calculation is not done successfully.
Return type: math_Matrix
`thisown`

The membership flag

`math_KronrodPointsAndWeights`(*args)
• Returns a vector of Kronrod points and a vector of their weights for Gauss-Kronrod computation method. Index should be odd and greater then or equal to 3, as the number of Kronrod points is equal to 2*N + 1, where N is a number of Gauss points. Points and Weights should have the size equal to Index. Each even element of Points represents a Gauss point value of N-th Gauss quadrature. The values from Index equal to 3 to 123 are stored in a table (see the file math_Kronrod.cxx). If Index is greater, then points and weights will be computed. Returns Standard_True if Index is odd, it is equal to the size of Points and Weights and the computation of Points and Weights is performed successfully. Otherwise this method returns Standard_False.
Parameters: Index (int) – Points (math_Vector &) – Weights (math_Vector &) – bool
`math_KronrodPointsMax`(*args)
• Returns the maximal number of points for that the values are stored in the table. If the number is greater then KronrodPointsMax, the points will be computed.
Return type: int
class `math_KronrodSingleIntegration`(*args)

Bases: `object`

• An empty constructor.
Return type: None
• Constructor. Takes the function, the lower and upper bound values, the initial number of Kronrod points
Parameters: theFunction (math_Function &) – theLower (float) – theUpper (float) – theNbPnts (int) – None
• Constructor. Takes the function, the lower and upper bound values, the initial number of Kronrod points, the tolerance value and the maximal number of iterations as parameters.
Parameters: theFunction (math_Function &) – theLower (float) – theUpper (float) – theNbPnts (int) – theTolerance (float) – theMaxNbIter (int) – None
`AbsolutError`()
• Returns the value of the relative error reached.
Return type: float
`ErrorReached`()
• Returns the value of the relative error reached.
Return type: float
static `GKRule`(*args)
Parameters: theFunction (math_Function &) – theLower (float) – theUpper (float) – theGaussP (math_Vector &) – theGaussW (math_Vector &) – theKronrodP (math_Vector &) – theKronrodW (math_Vector &) – theValue (float &) – theError (float &) – bool
`IsDone`()
• Returns Standard_True if computation is performed successfully.
Return type: bool
`NbIterReached`()
• Returns the number of iterations that were made to compute result.
Return type: int
`OrderReached`()
• Returns the number of Kronrod points for which the result is computed.
Return type: int
`Perform`()
• Computation of the integral. Takes the function, the lower and upper bound values, the initial number of Kronrod points, the relative tolerance value and the maximal number of iterations as parameters. theNbPnts should be odd and greater then or equal to 3.
Parameters: theFunction (math_Function &) – theLower (float) – theUpper (float) – theNbPnts (int) – None
• Computation of the integral. Takes the function, the lower and upper bound values, the initial number of Kronrod points, the relative tolerance value and the maximal number of iterations as parameters. theNbPnts should be odd and greater then or equal to 3. Note that theTolerance is relative, i.e. the criterion of solution reaching is: Abs(Kronrod - Gauss)/Abs(Kronrod) < theTolerance. theTolerance should be positive.
Parameters: theFunction (math_Function &) – theLower (float) – theUpper (float) – theNbPnts (int) – theTolerance (float) – theMaxNbIter (int) – None
`Value`()
• Returns the value of the integral.
Return type: float
`thisown`

The membership flag

`math_KronrodSingleIntegration_GKRule`(*args)
Parameters: theFunction (math_Function &) – theLower (float) – theUpper (float) – theGaussP (math_Vector &) – theGaussW (math_Vector &) – theKronrodP (math_Vector &) – theKronrodW (math_Vector &) – theValue (float &) – theError (float &) – bool
class `math_Matrix`(*args)

Bases: `object`

• Constructs a non-initialized matrix of range [LowerRow..UpperRow, LowerCol..UpperCol] For the constructed matrix: - LowerRow and UpperRow are the indexes of the lower and upper bounds of a row, and - LowerCol and UpperCol are the indexes of the lower and upper bounds of a column.
Parameters: LowerRow (int) – UpperRow (int) – LowerCol (int) – UpperCol (int) – None
• constructs a non-initialized matrix of range [LowerRow..UpperRow, LowerCol..UpperCol] whose values are all initialized with the value InitialValue.
Parameters: LowerRow (int) – UpperRow (int) – LowerCol (int) – UpperCol (int) – InitialValue (float) – None
• constructs a matrix of range [LowerRow..UpperRow, LowerCol..UpperCol] Sharing data with a ‘C array’ pointed by Tab.
Parameters: Tab (Standard_Address) – LowerRow (int) – UpperRow (int) – LowerCol (int) – UpperCol (int) – None
• constructs a matrix for copy in initialization. An exception is raised if the matrixes have not the same dimensions.
Parameters: Other (math_Matrix &) – None
`Add`()
• adds the matrix <Right> to a matrix. An exception is raised if the dimensions are different. Warning In order to save time when copying matrices, it is preferable to use operator += or the function Add whenever possible.
Parameters: Right (math_Matrix &) – None
• sets a matrix to the addition of <Left> and <Right>. An exception is raised if the dimensions are different.
Parameters: Left (math_Matrix &) – Right (math_Matrix &) – None
`Added`()
• adds the matrix <Right> to a matrix. An exception is raised if the dimensions are different.
Parameters: Right (math_Matrix &) – math_Matrix
`Col`()
• Returns the column of index <Col> of a matrix.
Parameters: Col (int) – math_Vector
`ColNumber`()
• Returns the number of rows of this matrix. Note that for a matrix A you always have the following relations: - A.RowNumber() = A.UpperRow() - A.LowerRow() + 1 - A.ColNumber() = A.UpperCol() - A.LowerCol() + 1 - the length of a row of A is equal to the number of columns of A, - the length of a column of A is equal to the number of rows of A.returns the row range of a matrix.
Return type: int
`Determinant`()
• Computes the determinant of a matrix. An exception is raised if the matrix is not a square matrix.
Return type: float
`Divide`()
• divides all the elements of a matrix by the value <Right>. An exception is raised if <Right> = 0.
Parameters: Right (float) – None
`Divided`()
• divides all the elements of a matrix by the value <Right>. An exception is raised if <Right> = 0.
Parameters: Right (float) – math_Matrix
`DumpToString`()

math_Matrix_DumpToString(math_Matrix self) -> std::string

`Init`()
• Initialize all the elements of a matrix to InitialValue.
Parameters: InitialValue (float) – None
`Initialized`()
• Matrixes are copied through assignement. An exception is raised if the dimensions are differents.
Parameters: Other (math_Matrix &) – math_Matrix
`Inverse`()
• Returns the inverse of a matrix. Exception NotSquare is raised if the matrix is not square. Exception SingularMatrix is raised if the matrix is singular.
Return type: math_Matrix
`Invert`()
• Inverts a matrix using Gauss algorithm. Exception NotSquare is raised if the matrix is not square. Exception SingularMatrix is raised if the matrix is singular.
Return type: None
`LowerCol`()
• Returns the value of the Lower index of the column range of a matrix.
Return type: int
`LowerRow`()
• Returns the value of the Lower index of the row range of a matrix.
Return type: int
`Multiplied`()
• multiplies all the elements of a matrix by the value <Right>.
Parameters: Right (float) – math_Matrix
• Returns the product of 2 matrices. An exception is raised if the dimensions are different.
Parameters: Right (math_Matrix &) – math_Matrix
• Returns the product of a matrix by a vector. An exception is raised if the dimensions are different.
Parameters: Right (math_Vector &) – math_Vector
`Multiply`()
• Sets this matrix to the product of the matrix Left, and the matrix Right. Example math_Matrix A (1, 3, 1, 3); math_Matrix B (1, 3, 1, 3); // A = ... , B = ... math_Matrix C (1, 3, 1, 3); C.Multiply(A, B); Exceptions Standard_DimensionError if matrices are of incompatible dimensions, i.e. if: - the number of columns of matrix Left, or the number of rows of matrix TLeft is not equal to the number of rows of matrix Right, or - the number of rows of matrix Left, or the number of columns of matrix TLeft is not equal to the number of rows of this matrix, or - the number of columns of matrix Right is not equal to the number of columns of this matrix.
Parameters: Right (float) – None
• Computes a matrix as the product of 2 vectors. An exception is raised if the dimensions are different. <self> = <Left> * <Right>.
Parameters: Left (math_Vector &) – Right (math_Vector &) – None
• Computes a matrix as the product of 2 matrixes. An exception is raised if the dimensions are different.
Parameters: Left (math_Matrix &) – Right (math_Matrix &) – None
• Returns the product of 2 matrices. An exception is raised if the dimensions are different.
Parameters: Right (math_Matrix &) – None
`Opposite`()
• Returns the opposite of a matrix. An exception is raised if the dimensions are different.
Return type: math_Matrix
`Row`()
• Returns the row of index Row of a matrix.
Parameters: Row (int) – math_Vector
`RowNumber`()
• Returns the number of rows of this matrix. Note that for a matrix A you always have the following relations: - A.RowNumber() = A.UpperRow() - A.LowerRow() + 1 - A.ColNumber() = A.UpperCol() - A.LowerCol() + 1 - the length of a row of A is equal to the number of columns of A, - the length of a column of A is equal to the number of rows of A.returns the row range of a matrix.
Return type: int
`Set`()
• Sets the values of this matrix, - from index I1 to index I2 on the row dimension, and - from index J1 to index J2 on the column dimension, to those of matrix M. Exceptions Standard_DimensionError if: - I1 is less than the index of the lower row bound of this matrix, or - I2 is greater than the index of the upper row bound of this matrix, or - J1 is less than the index of the lower column bound of this matrix, or - J2 is greater than the index of the upper column bound of this matrix, or - I2 - I1 + 1 is not equal to the number of rows of matrix M, or - J2 - J1 + 1 is not equal to the number of columns of matrix M.
Parameters: I1 (int) – I2 (int) – J1 (int) – J2 (int) – M (math_Matrix &) – Other (math_Matrix &) – None math_Matrix
`SetCol`()
• Sets the column of index Col of a matrix to the vector <V>. An exception is raised if the dimensions are different. An exception is raises if <Col> is inferior to the lower column of the matrix or <Col> is superior to the upper column.
Parameters: Col (int) – V (math_Vector &) – None
`SetDiag`()
• Sets the diagonal of a matrix to the value <Value>. An exception is raised if the matrix is not square.
Parameters: Value (float) – None
`SetRow`()
• Sets the row of index Row of a matrix to the vector <V>. An exception is raised if the dimensions are different. An exception is raises if <Row> is inferior to the lower row of the matrix or <Row> is superior to the upper row.
Parameters: Row (int) – V (math_Vector &) – None
`Subtract`()
• Subtracts the matrix <Right> from <self>. An exception is raised if the dimensions are different. Warning In order to avoid time-consuming copying of matrices, it is preferable to use operator -= or the function Subtract whenever possible.
Parameters: Right (math_Matrix &) – None
• Sets a matrix to the Subtraction of the matrix <Right> from the matrix <Left>. An exception is raised if the dimensions are different.
Parameters: Left (math_Matrix &) – Right (math_Matrix &) – None
`Subtracted`()
• Returns the result of the subtraction of <Right> from <self>. An exception is raised if the dimensions are different.
Parameters: Right (math_Matrix &) – math_Matrix
`SwapCol`()
• Swaps the columns of index <Col1> and <Col2>. An exception is raised if <Col1> or <Col2> is out of range.
Parameters: Col1 (int) – Col2 (int) – None
`SwapRow`()
• Swaps the rows of index Row1 and Row2. An exception is raised if <Row1> or <Row2> is out of range.
Parameters: Row1 (int) – Row2 (int) – None
`TMultiplied`()
• Sets this matrix to the product of the transposed matrix TLeft, and the matrix Right. Example math_Matrix A (1, 3, 1, 3); math_Matrix B (1, 3, 1, 3); // A = ... , B = ... math_Matrix C (1, 3, 1, 3); C.Multiply(A, B); Exceptions Standard_DimensionError if matrices are of incompatible dimensions, i.e. if: - the number of columns of matrix Left, or the number of rows of matrix TLeft is not equal to the number of rows of matrix Right, or - the number of rows of matrix Left, or the number of columns of matrix TLeft is not equal to the number of rows of this matrix, or - the number of columns of matrix Right is not equal to the number of columns of this matrix.
Parameters: Right (float) – math_Matrix
`TMultiply`()
• Returns the product of the transpose of a matrix with the matrix <Right>. An exception is raised if the dimensions are different.
Parameters: Right (math_Matrix &) – math_Matrix
• Computes a matrix to the product of the transpose of the matrix <TLeft> with the matrix <Right>. An exception is raised if the dimensions are different.
Parameters: TLeft (math_Matrix &) – Right (math_Matrix &) – None
`Transpose`()
• Transposes a given matrix. An exception is raised if the matrix is not a square matrix.
Return type: None
`Transposed`()
• Teturns the transposed of a matrix. An exception is raised if the matrix is not a square matrix.
Return type: math_Matrix
`UpperCol`()
• Returns the value of the upper index of the column range of a matrix.
Return type: int
`UpperRow`()
• Returns the Upper index of the row range of a matrix.
Return type: int
`Value`()
• Accesses (in read or write mode) the value of index <Row> and <Col> of a matrix. An exception is raised if <Row> and <Col> are not in the correct range.
Parameters: Row (int) – Col (int) – float
`thisown`

The membership flag

class `math_MultipleVarFunction`(*args, **kwargs)

Bases: `object`

`Delete`()
Return type: void
`GetStateNumber`()
• return the state of the function corresponding to the latestt call of any methods associated to the function. This function is called by each of the algorithms described later which define the function Integer Algorithm::StateNumber(). The algorithm has the responsibility to call this function when it has found a solution (i.e. a root or a minimum) and has to maintain the association between the solution found and this StateNumber. Byu default, this method returns 0 (which means for the algorithm: no state has been saved). It is the responsibility of the programmer to decide if he needs to save the current state of the function and to return an Integer that allows retrieval of the state.
Return type: int
`NbVariables`()
• Returns the number of variables of the function
Return type: int
`Value`()
• Computes the values of the Functions <F> for the variable <X>. returns True if the computation was done successfully, otherwise false.
Parameters: X (math_Vector &) – F (float &) – bool
`thisown`

The membership flag

class `math_MultipleVarFunctionWithGradient`(*args, **kwargs)
`Gradient`()
• Computes the gradient <G> of the functions for the variable <X>. Returns True if the computation was done successfully, False otherwise.
Parameters: X (math_Vector &) – G (math_Vector &) – bool
`Values`()
• computes the value <F> and the gradient <G> of the functions for the variable <X>. Returns True if the computation was done successfully, False otherwise.
Parameters: X (math_Vector &) – F (float &) – G (math_Vector &) – bool
`thisown`

The membership flag

class `math_MultipleVarFunctionWithHessian`(*args, **kwargs)
`Values`()
• computes the value <F> and the gradient <G> of the functions for the variable <X>. Returns True if the computation was done successfully, False otherwise.
Parameters: X (math_Vector &) – F (float &) – G (math_Vector &) – bool
• computes the value <F>, the gradient <G> and the hessian <H> of the functions for the variable <X>. Returns True if the computation was done successfully, False otherwise.
Parameters: X (math_Vector &) – F (float &) – G (math_Vector &) – H (math_Matrix &) – bool
`thisown`

The membership flag

class `math_NewtonFunctionRoot`(*args)

Bases: `object`

• The Newton method is done to find the root of the function F from the initial guess Guess. The tolerance required on the root is given by Tolerance. The solution is found when : abs(Xi - Xi-1) <= EpsX and abs(F(Xi))<= EpsF The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_FunctionWithDerivative &) – Guess (float) – EpsX (float) – EpsF (float) – NbIterations (int) – default value is 100 None
• The Newton method is done to find the root of the function F from the initial guess Guess. The solution must be inside the interval [A, B]. The tolerance required on the root is given by Tolerance. The solution is found when : abs(Xi - Xi-1) <= EpsX and abs(F(Xi))<= EpsF The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_FunctionWithDerivative &) – Guess (float) – EpsX (float) – EpsF (float) – A (float) – B (float) – NbIterations (int) – default value is 100 None
• is used in a sub-class to initialize correctly all the fields of this class.
Parameters: A (float) – B (float) – EpsX (float) – EpsF (float) – NbIterations (int) – default value is 100 None
`Derivative`()
• returns the value of the derivative at the root. Exception NotDone is raised if the root was not found.
Return type: float
`DumpToString`()

math_NewtonFunctionRoot_DumpToString(math_NewtonFunctionRoot self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`NbIterations`()
• Returns the number of iterations really done on the computation of the Root. Exception NotDone is raised if the root was not found.
Return type: int
`Perform`()
• is used internally by the constructors.
Parameters: F (math_FunctionWithDerivative &) – Guess (float) – None
`Root`()
• Returns the value of the root of function <F>. Exception NotDone is raised if the root was not found.
Return type: float
`Value`()
• returns the value of the function at the root. Exception NotDone is raised if the root was not found.
Return type: float
`thisown`

The membership flag

class `math_NewtonFunctionSetRoot`(*args)

Bases: `object`

• This constructor should be used in a sub-class to initialize correctly all the fields of this class. The range (1, F.NbVariables()) must be especially respected for all vectors and matrix declarations.
Parameters: F (math_FunctionSetWithDerivatives &) – XTol (math_Vector &) – FTol (float) – NbIterations (int) – default value is 100 None
• This constructor should be used in a sub-class to initialize correctly all the fields of this class. The range (1, F.NbVariables()) must be especially respected for all vectors and matrix declarations. The method SetTolerance must be called before performing the algorithm.
Parameters: F (math_FunctionSetWithDerivatives &) – FTol (float) – NbIterations (int) – default value is 100 None
• The Newton method is done to improve the root of the function F from the initial guess StartingPoint. The tolerance required on the root is given by Tolerance. The solution is found when : abs(Xj - Xj-1)(i) <= XTol(i) and abs(Fi) <= FTol for all i; The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_FunctionSetWithDerivatives &) – StartingPoint (math_Vector &) – XTol (math_Vector &) – FTol (float) – NbIterations (int) – default value is 100 None
• The Newton method is done to improve the root of the function F from the initial guess StartingPoint. The tolerance required on the root is given by Tolerance. The solution is found when : abs(Xj - Xj-1)(i) <= XTol(i) and abs(Fi) <= FTol for all i; The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_FunctionSetWithDerivatives &) – StartingPoint (math_Vector &) – InfBound (math_Vector &) – SupBound (math_Vector &) – XTol (math_Vector &) – FTol (float) – NbIterations (int) – default value is 100 None
`Delete`()
Return type: void
`Derivative`()
• Returns the matrix value of the derivative at the root. Exception NotDone is raised if the root was not found.
Return type: math_Matrix
• Outputs the matrix value of the derivative at the root in Der. Exception NotDone is raised if the root was not found. Exception DimensionError is raised if the range of Der is not equal to the range of the StartingPoint.
Parameters: Der (math_Matrix &) – None
`DumpToString`()

math_NewtonFunctionSetRoot_DumpToString(math_NewtonFunctionSetRoot self) -> std::string

`FunctionSetErrors`()
• Returns the vector value of the error done on the functions at the root. Exception NotDone is raised if the root was not found.
Return type: math_Vector
• Outputs the vector value of the error done on the functions at the root in Err. Exception NotDone is raised if the root was not found. Exception DimensionError is raised if the range of Err is not equal to the range of the StartingPoint.
Parameters: Err (math_Vector &) – None
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`IsSolutionReached`()
• This method is called at the end of each iteration to check if the solution is found. Vectors DeltaX, Fvalues and Jacobian Matrix are consistent with the possible solution Vector Sol and can be inspected to decide whether the solution is reached or not.
Parameters: F (math_FunctionSetWithDerivatives &) – bool
`NbIterations`()
• Returns the number of iterations really done during the computation of the Root. Exception NotDone is raised if the root was not found.
Return type: int
`Perform`()
• Improves the root of function F from the initial guess StartingPoint. infBound and supBound may be given, to constrain the solution. Warning This method must be called when the solution is not computed by the constructors.
Parameters: F (math_FunctionSetWithDerivatives &) – StartingPoint (math_Vector &) – InfBound (math_Vector &) – SupBound (math_Vector &) – None
`Root`()
• Returns the value of the root of function F. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false).
Return type: math_Vector
• outputs the root vector in Root. Exception NotDone is raised if the root was not found. Exception DimensionError is raised if the range of Root is not equal to the range of the StartingPoint.
Parameters: Root (math_Vector &) – None
`SetTolerance`()
• Initializes the tolerance values for the unknowns.
Parameters: XTol (math_Vector &) – None
`thisown`

The membership flag

class `math_NewtonMinimum`(*args)

Bases: `object`

• – Given the starting point StartingPoint, The tolerance required on the solution is given by Tolerance. Iteration are stopped if (!WithSingularity) and H(F(Xi)) is not definite positive (if the smaller eigenvalue of H < Convexity) or IsConverged() returns True for 2 successives Iterations. Warning: Obsolete Constructor (because IsConverged can not be redefined with this. )
Parameters: F (math_MultipleVarFunctionWithHessian &) – StartingPoint (math_Vector &) – Tolerance (float) – default value is 1.0e-7 NbIterations (int) – default value is 40 Convexity (float) – default value is 1.0e-6 WithSingularity (bool) – default value is Standard_True None
• The tolerance required on the solution is given by Tolerance. Iteration are stopped if (!WithSingularity) and H(F(Xi)) is not definite positive (if the smaller eigenvalue of H < Convexity) or IsConverged() returns True for 2 successives Iterations. Warning: This constructor do not computation
Parameters: F (math_MultipleVarFunctionWithHessian &) – Tolerance (float) – default value is 1.0e-7 NbIterations (int) – default value is 40 Convexity (float) – default value is 1.0e-6 WithSingularity (bool) – default value is Standard_True None
`Delete`()
Return type: void
`DumpToString`()

math_NewtonMinimum_DumpToString(math_NewtonMinimum self) -> std::string

`Gradient`()
• returns the gradient vector at the minimum. Exception NotDone is raised if an error has occured.the minimum was not found.
Return type: math_Vector
• outputs the gradient vector at the minimum in Grad. Exception NotDone is raised if the minimum was not found. Exception DimensionError is raised if the range of Grad is not equal to the range of the StartingPoint.
Parameters: Grad (math_Vector &) – None
`IsConverged`()
• This method is called at the end of each iteration to check the convergence : || Xi+1 - Xi || < Tolerance or || F(Xi+1) - F(Xi)|| < Tolerance * || F(Xi) || It can be redefined in a sub-class to implement a specific test.
Return type: bool
`IsDone`()
• Tests if an error has occured.
Return type: bool
`Location`()
• returns the location vector of the minimum. Exception NotDone is raised if an error has occured.
Return type: math_Vector
• outputs the location vector of the minimum in Loc. Exception NotDone is raised if an error has occured. Exception DimensionError is raised if the range of Loc is not equal to the range of the StartingPoint.
Parameters: Loc (math_Vector &) – None
`Minimum`()
• returns the value of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: float
`NbIterations`()
• returns the number of iterations really done in the calculation of the minimum. The exception NotDone is raised if an error has occured.
Return type: int
`Perform`()
• Search the solution.
Parameters: F (math_MultipleVarFunctionWithHessian &) – StartingPoint (math_Vector &) – None
`thisown`

The membership flag

`math_OrderedGaussPointsAndWeights`(*args)
• Returns a vector of Gauss points and a vector of their weights. The difference with the method GaussPoints is the following: - the points are returned in increasing order. - if Index is greater then GaussPointsMax, the points are computed. Returns Standard_True if Index is positive, Points’ and Weights’ length is equal to Index, Points and Weights are successfully computed.
Parameters: Index (int) – Points (math_Vector &) – Weights (math_Vector &) – bool
class `math_PSO`(*args)

Bases: `object`

• /** * Constructor. * * @param theFunc defines the objective function. It should exist during all lifetime of class instance. * @param theLowBorder defines lower border of search space. * @param theUppBorder defines upper border of search space. * @param theSteps defines steps of regular grid, used for particle generation. This parameter used to define stop condition (TerminalVelocity). * @param theNbParticles defines number of particles. * @param theNbIter defines maximum number of iterations. */
Parameters: theFunc (math_MultipleVarFunction *) – theLowBorder (math_Vector &) – theUppBorder (math_Vector &) – theSteps (math_Vector &) – theNbParticles (int) – default value is 32 theNbIter (int) – default value is 100 None
`Perform`()
• Perform computations, particles array is constructed inside of this function.
Parameters: theSteps (math_Vector &) – theValue (float &) – theOutPnt (math_Vector &) – theNbIter (int) – default value is 100 None
• Perform computations with given particles array.
Parameters: theParticles (math_PSOParticlesPool &) – theNbParticles (int) – theValue (float &) – theOutPnt (math_Vector &) – theNbIter (int) – default value is 100 None
`thisown`

The membership flag

class `math_PSOParticlesPool`(*args)

Bases: `object`

Parameters: theParticlesCount (int) – theDimensionCount (int) – None
`GetBestParticle`()
Return type: PSO_Particle *
`GetParticle`()
Parameters: theIdx (int) – PSO_Particle *
`GetWorstParticle`()
Return type: PSO_Particle *
`thisown`

The membership flag

class `math_Powell`(*args)

Bases: `object`

• Computes Powell minimization on the function F given StartingPoint, and an initial matrix StartingDirection whose columns contain the initial set of directions. The solution F = Fi is found when 2.0 * abs(Fi - Fi-1) = <Tolerance * (abs(Fi) + abs(Fi-1) + ZEPS). The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_MultipleVarFunction &) – StartingPoint (math_Vector &) – StartingDirections (math_Matrix &) – Tolerance (float) – NbIterations (int) – default value is 200 ZEPS (float) – default value is 1.0e-12 None
• is used in a sub-class to initialize correctly all the fields of this class.
Parameters: F (math_MultipleVarFunction &) – Tolerance (float) – NbIterations (int) – default value is 200 ZEPS (float) – default value is 1.0e-12 None
`Delete`()
Return type: void
`DumpToString`()

math_Powell_DumpToString(math_Powell self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`IsSolutionReached`()
• solution F = Fi is found when : 2.0 * abs(Fi - Fi-1) <= Tolerance * (abs(Fi) + abs(Fi-1)) + ZEPS. The maximum number of iterations allowed is given by NbIterations.
Parameters: F (math_MultipleVarFunction &) – bool
`Location`()
• returns the location vector of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: math_Vector
• outputs the location vector of the minimum in Loc. Exception NotDone is raised if the minimum was not found. Exception DimensionError is raised if the range of Loc is not equal to the range of the StartingPoint.
Parameters: Loc (math_Vector &) – None
`Minimum`()
• Returns the value of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: float
`NbIterations`()
• Returns the number of iterations really done during the computation of the minimum. Exception NotDone is raised if the minimum was not found.
Return type: int
`Perform`()
• Use this method after a call to the initialization constructor to compute the minimum of function F. Warning The initialization constructor must have been called before the Perform method is called.
Parameters: F (math_MultipleVarFunction &) – StartingPoint (math_Vector &) – StartingDirections (math_Matrix &) – None
`thisown`

The membership flag

class `math_QuickSortOfValueAndWeight`

Bases: `object`

static `Sort`(*args)
Parameters: TheArray (math_Array1OfValueAndWeight &) – Comp (math_CompareOfValueAndWeight &) – void
`thisown`

The membership flag

`math_QuickSortOfValueAndWeight_Sort`(*args)
Parameters: TheArray (math_Array1OfValueAndWeight &) – Comp (math_CompareOfValueAndWeight &) – void
class `math_RealRandom`(*args)

Bases: `object`

• creates a real random generator between the values Lower and Upper.
Parameters: Lower (float) – Upper (float) – None
`Next`()
• returns the next random number.
Return type: float
`Reset`()
• reinitializes the random generator
Return type: None
`thisown`

The membership flag

class `math_SVD`(*args)

Bases: `object`

• Given as input an n X m matrix A with n < m, n = m or n > m this constructor performs the Singular Value Decomposition.
Parameters: A (math_Matrix &) – None
`DumpToString`()

math_SVD_DumpToString(math_SVD self) -> std::string

`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`PseudoInverse`()
• Computes the inverse Inv of matrix A such as A * Inverse = Identity. Exceptions StdFail_NotDone if the algorithm fails (and IsDone returns false). Standard_DimensionError if the ranges of Inv are compatible with the ranges of A.
Parameters: Inv (math_Matrix &) – Eps (float) – default value is 1.0e-6 None
`Solve`()
• Given the input Vector B this routine solves the set of linear equations A . X = B. Exception NotDone is raised if the decomposition of A was not done successfully. Exception DimensionError is raised if the range of B is not equal to the rowrange of A. Exception DimensionError is raised if the range of X is not equal to the colrange of A.
Parameters: B (math_Vector &) – X (math_Vector &) – Eps (float) – default value is 1.0e-6 None
`thisown`

The membership flag

class `math_TrigonometricFunctionRoots`(*args)

Bases: `object`

• Given coefficients a, b, c, d , e, this constructor performs the resolution of the equation above. The solutions must be contained in [InfBound, SupBound]. InfBound and SupBound can be set by default to 0 and 2*PI.
Parameters: A (float) – B (float) – C (float) – D (float) – E (float) – InfBound (float) – SupBound (float) – None
• Given the two coefficients d and e, it performs the resolution of d*sin(x) + e = 0. The solutions must be contained in [InfBound, SupBound]. InfBound and SupBound can be set by default to 0 and 2*PI.
Parameters: D (float) – E (float) – InfBound (float) – SupBound (float) – None
• Given the three coefficients c, d and e, it performs the resolution of 2*b*cos(x)*sin(x) + d*sin(x) + e = 0. The solutions must be contained in [InfBound, SupBound]. InfBound and SupBound can be set by default to 0 and 2*PI.
Parameters: C (float) – D (float) – E (float) – InfBound (float) – SupBound (float) – None
`DumpToString`()

math_TrigonometricFunctionRoots_DumpToString(math_TrigonometricFunctionRoots self) -> std::string

`InfiniteRoots`()
• Returns true if there is an infinity of roots, otherwise returns false.
Return type: bool
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`NbSolutions`()
• Returns the number of solutions found. An exception is raised if NotDone. An exception is raised if there is an infinity of solutions.
Return type: int
`Value`()
• Returns the solution of range Index. An exception is raised if NotDone. An exception is raised if Index>NbSolutions. An exception is raised if there is an infinity of solutions.
Parameters: Index (int) – float
`thisown`

The membership flag

class `math_Uzawa`(*args)

Bases: `object`

• Given an input matrix Cont, two input vectors Secont and StartingPoint, it solves Cont*X = Secont (only = equations) with a minimization of Norme(X-X0). The maximun iterations number allowed is fixed to NbIterations. The tolerance EpsLic is fixed for the dual variable convergence. The tolerance EpsLix is used for the convergence of X. Exception ConstuctionError is raised if the line number of Cont is different from the length of Secont.
Parameters: Cont (math_Matrix &) – Secont (math_Vector &) – StartingPoint (math_Vector &) – EpsLix (float) – default value is 1.0e-06 EpsLic (float) – default value is 1.0e-06 NbIterations (int) – default value is 500 None
• Given an input matrix Cont, two input vectors Secont and StartingPoint, it solves Cont*X = Secont (the Nce first equations are equal equations and the Nci last equations are inequalities <) with a minimization of Norme(X-X0). The maximun iterations number allowed is fixed to NbIterations. The tolerance EpsLic is fixed for the dual variable convergence. The tolerance EpsLix is used for the convergence of X. There are no conditions on Nce and Nci. Exception ConstuctionError is raised if the line number of Cont is different from the length of Secont and from Nce + Nci.
Parameters: Cont (math_Matrix &) – Secont (math_Vector &) – StartingPoint (math_Vector &) – Nci (int) – Nce (int) – EpsLix (float) – default value is 1.0e-06 EpsLic (float) – default value is 1.0e-06 NbIterations (int) – default value is 500 None
`Duale`()
• returns the duale variables V of the systeme.
Parameters: V (math_Vector &) – None
`DumpToString`()

math_Uzawa_DumpToString(math_Uzawa self) -> std::string

`Error`()
• Returns the difference between X solution and the StartingPoint. An exception is raised if NotDone.
Return type: math_Vector
`InitialError`()
• Returns the initial error Cont*StartingPoint-Secont. An exception is raised if NotDone.
Return type: math_Vector
`InverseCont`()
• returns the inverse matrix of (C * Transposed(C)). This result is needed for the computation of the gradient when approximating a curve.
Return type: math_Matrix
`IsDone`()
• Returns true if the computations are successful, otherwise returns false.
Return type: bool
`NbIterations`()
• returns the number of iterations really done. An exception is raised if NotDone.
Return type: int
`Value`()
• Returns the vector solution of the system above. An exception is raised if NotDone.
Return type: math_Vector
`thisown`

The membership flag

class `math_ValueAndWeight`(*args)

Bases: `object`

Return type: None Value (float) – Weight (float) – None
`Value`()
Return type: float
`Weight`()
Return type: float
`thisown`

The membership flag

class `math_Vector`(*args)

Bases: `object`

• Contructs a non-initialized vector in the range [theLower..theUpper] ‘theLower’ and ‘theUpper’ are the indexes of the lower and upper bounds of the constructed vector.
Parameters: theLower (int) – theUpper (int) – None
• Contructs a vector in the range [theLower..theUpper] whose values are all initialized with the value ‘theInitialValue’
Parameters: theLower (int) – theUpper (int) – theInitialValue (float) – None
• Constructs a vector in the range [theLower..theUpper] with the ‘c array’ theTab.
Parameters: theTab (Standard_Address) – theLower (int) – theUpper (int) – None
• Constructs a copy for initialization. An exception is raised if the lengths of the vectors are different.
Parameters: theOther (math_Vector &) – None
`Add`()
• adds the vector ‘theRight’ to a vector. An exception is raised if the vectors have not the same length. Warning In order to avoid time-consuming copying of vectors, it is preferable to use operator += or the function Add whenever possible.
Parameters: theRight (math_Vector &) – None
• sets a vector to the sum of the vector ‘theLeft’ and the vector ‘theRight’. An exception is raised if the lengths are different.
Parameters: theLeft (math_Vector &) – theRight (math_Vector &) – None
`Added`()
• adds the vector theRight to a vector. An exception is raised if the vectors have not the same length. An exception is raised if the lengths are not equal.
Parameters: theRight (math_Vector &) – math_Vector
`Divide`()
• divides a vector by the value ‘theRight’. An exception is raised if ‘theRight’ = 0.
Parameters: theRight (float) – None
`Divided`()
• divides a vector by the value ‘theRight’. An exception is raised if ‘theRight’ = 0.
Parameters: theRight (float) – math_Vector
`DumpToString`()

math_Vector_DumpToString(math_Vector self) -> std::string

`Init`()
• Initialize all the elements of a vector with ‘theInitialValue’.
Parameters: theInitialValue (float) – None
`Initialized`()
• Initialises a vector by copying ‘theOther’. An exception is raised if the Lengths are differents.
Parameters: theOther (math_Vector &) – math_Vector
`Inverse`()
• Inverts this vector and creates a new vector.
Return type: math_Vector
`Invert`()
• Inverts this vector and assigns the result to this vector.
Return type: None
`Length`()
• Returns the length of a vector
Return type: inline int
`Lower`()
• Returns the value of the theLower index of a vector.
Return type: inline int
`Max`()
• Returns the value of the ‘Index’ of the maximum element of a vector.
Return type: int
`Min`()
• Returns the value of the ‘Index’ of the minimum element of a vector.
Return type: int
`Multiplied`()
• returns the product of a vector and a real value.
Parameters: theRight (float) – math_Vector
• returns the inner product of 2 vectors. An exception is raised if the lengths are not equal.
Parameters: theRight (math_Vector &) – float
• returns the product of a vector by a matrix.
Parameters: theRight (math_Matrix &) – math_Vector
`Multiply`()
• returns the product of a vector and a real value.
Parameters: theRight (float) – None
• sets a vector to the product of the vector ‘theLeft’ with the matrix ‘theRight’.
Parameters: theLeft (math_Vector &) – theRight (math_Matrix &) – None
• //!sets a vector to the product of the matrix ‘theLeft’ with the vector ‘theRight’.
Parameters: theLeft (math_Matrix &) – theRight (math_Vector &) – None
• returns the multiplication of a real by a vector. ‘me’ = ‘theLeft’ * ‘theRight’
Parameters: theLeft (float) – theRight (math_Vector &) – None
`Norm`()
• Returns the value or the square of the norm of this vector.
Return type: float
`Norm2`()
• Returns the value of the square of the norm of a vector.
Return type: float
`Normalize`()
• Normalizes this vector (the norm of the result is equal to 1.0) and assigns the result to this vector Exceptions Standard_NullValue if this vector is null (i.e. if its norm is less than or equal to Standard_Real::RealEpsilon().
Return type: None
`Normalized`()
• Normalizes this vector (the norm of the result is equal to 1.0) and creates a new vector Exceptions Standard_NullValue if this vector is null (i.e. if its norm is less than or equal to Standard_Real::RealEpsilon().
Return type: math_Vector
`Opposite`()
• returns the opposite of a vector.
Return type: math_Vector
`Set`()
• sets a vector from ‘theI1’ to ‘theI2’ to the vector ‘theV’; An exception is raised if ‘theI1’ is less than ‘LowerIndex’ or ‘theI2’ is greater than ‘UpperIndex’ or ‘theI1’ is greater than ‘theI2’. An exception is raised if ‘theI2-theI1+1’ is different from the ‘Length’ of ‘theV’.
Parameters: theI1 (int) – theI2 (int) – theV (math_Vector &) – theOther (math_Vector &) – None math_Vector
`Slice`()
• //!Creates a new vector by inverting the values of this vector between indexes ‘theI1’ and ‘theI2’. If the values of this vector were (1., 2., 3., 4.,5., 6.), by slicing it between indexes 2 and 5 the values of the resulting vector are (1., 5., 4., 3., 2., 6.)
Parameters: theI1 (int) – theI2 (int) – math_Vector
`Subtract`()
• sets a vector to the Subtraction of the vector theRight from the vector theLeft. An exception is raised if the vectors have not the same length. Warning In order to avoid time-consuming copying of vectors, it is preferable to use operator -= or the function Subtract whenever possible.
Parameters: theLeft (math_Vector &) – theRight (math_Vector &) – None
• returns the subtraction of ‘theRight’ from ‘me’. An exception is raised if the vectors have not the same length.
Parameters: theRight (math_Vector &) – None
`Subtracted`()
• returns the subtraction of ‘theRight’ from ‘me’. An exception is raised if the vectors have not the same length.
Parameters: theRight (math_Vector &) – math_Vector
`TMultiplied`()
• returns the product of a vector and a real value.
Parameters: theRight (float) – math_Vector
`TMultiply`()
• sets a vector to the product of the transpose of the matrix ‘theTLeft’ by the vector ‘theRight’.
Parameters: theTLeft (math_Matrix &) – theRight (math_Vector &) – None
• sets a vector to the product of the vector ‘theLeft’ by the transpose of the matrix ‘theTRight’.
Parameters: theLeft (math_Vector &) – theTRight (math_Matrix &) – None
`Upper`()
• Returns the value of the theUpper index of a vector.
Return type: inline int
`Value`()
• accesses (in read or write mode) the value of index ‘theNum’ of a vector.
Parameters: theNum (int) – inline float
`thisown`

The membership flag

`register_handle`(handle, base_object)

Inserts the handle into the base object to prevent memory corruption in certain cases