Todo
Something concise here.
miniEigen is wrapper for a small part of the Eigen library. Refer to its documentation for details. All classes in this module support pickling.
Axis-aligned box object in 2d, defined by its minimum and maximum corners
contains( (AlignedBox2)arg1, (AlignedBox2)arg2) → bool
extend( (AlignedBox2)arg1, (AlignedBox2)arg2) → None
Axis-aligned box object, defined by its minimum and maximum corners
contains( (AlignedBox3)arg1, (AlignedBox3)arg2) → bool
extend( (AlignedBox3)arg1, (AlignedBox3)arg2) → None
3x3 float matrix.
Supported operations (m is a Matrix3, f if a float/int, v is a Vector3): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.
Static attributes: Zero, Ones, Identity.
Return an object where all elements are randomly set to values between 0 and 1.
Return column as vector.
Number of columns.
Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).
Return matrix determinant.
Return diagonal as vector.
Return inverted matrix.
Approximate comparison with precision prec.
Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Alias for computeUnitaryPositive.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Return row as vector.
Number of rows.
Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().
Alias for selfAdjointEigenDecomposition.
Square of the Euclidean norm.
Sum of all elements.
Return sum of diagonal elements.
Return transposed matrix.
/TODO/
Return an object where all elements are randomly set to values between 0 and 1.
Return column as vector.
Number of columns.
Return matrix determinant.
Return diagonal as vector.
Return inverted matrix.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Mean value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Return row as vector.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
Return sum of diagonal elements.
Return transposed matrix.
6x6 float matrix. Constructed from 4 3x3 sub-matrices, from 6xVector6 (rows).
Supported operations (m is a Matrix6, f if a float/int, v is a Vector6): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.
Static attributes: Zero, Ones, Identity.
Return an object where all elements are randomly set to values between 0 and 1.
Return column as vector.
Number of columns.
Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).
Return matrix determinant.
Return diagonal as vector.
Return inverted matrix.
Approximate comparison with precision prec.
Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()
Return lower-left 3x3 block
Return lower-right 3x3 block
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Alias for computeUnitaryPositive.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Return row as vector.
Number of rows.
Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().
Alias for selfAdjointEigenDecomposition.
Square of the Euclidean norm.
Sum of all elements.
Return sum of diagonal elements.
Return transposed matrix.
Return upper-left 3x3 block
Return upper-right 3x3 block
/TODO/
Return an object where all elements are randomly set to values between 0 and 1.
Return column as vector.
Number of columns.
Return matrix determinant.
Return diagonal as vector.
Return inverted matrix.
Approximate comparison with precision prec.
Return lower-left 3x3 block
Return lower-right 3x3 block
Maximum absolute value over all elements.
Mean value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Return row as vector.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
Return sum of diagonal elements.
Return transposed matrix.
Return upper-left 3x3 block
Return upper-right 3x3 block
XxX (dynamic-sized) float matrix. Constructed from list of rows (as VectorX).
Supported operations (m is a MatrixX, f if a float/int, v is a VectorX): -m, m+m, m+=m, m-m, m-=m, m*f, f*m, m*=f, m/f, m/=f, m*m, m*=m, m*v, v*m, m==m, m!=m.
Create identity matrix with given rank (square).
Create matrix of given dimensions where all elements are set to 1.
Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).
Create zero matrix of given dimensions
Return column as vector.
Number of columns.
Compute polar decomposition (unitary matrix U and positive semi-definite symmetric matrix P such that self=U*P).
Return matrix determinant.
Return diagonal as vector.
Return inverted matrix.
Approximate comparison with precision prec.
Compute SVD decomposition of square matrix, retuns (U,S,V) such that self=U*S*V.transpose()
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Alias for computeUnitaryPositive.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Change size of the matrix, keep values of elements which exist in the new matrix
Return row as vector.
Number of rows.
Compute eigen (spectral) decomposition of symmetric matrix, returns (eigVecs,eigVals). eigVecs is orthogonal Matrix3 with columns ar normalized eigenvectors, eigVals is Vector3 with corresponding eigenvalues. self=eigVecs*diag(eigVals)*eigVecs.transpose().
Alias for selfAdjointEigenDecomposition.
Square of the Euclidean norm.
Sum of all elements.
Return sum of diagonal elements.
Return transposed matrix.
/TODO/
Create identity matrix with given rank (square).
Create matrix of given dimensions where all elements are set to 1.
Create matrix with given dimensions where all elements are set to number between 0 and 1 (uniformly-distributed).
Create zero matrix of given dimensions
Return column as vector.
Number of columns.
Return matrix determinant.
Return diagonal as vector.
Return inverted matrix.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Mean value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Change size of the matrix, keep values of elements which exist in the new matrix
Return row as vector.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
Return sum of diagonal elements.
Return transposed matrix.
Quaternion representing rotation.
Supported operations (q is a Quaternion, v is a Vector3): q*q (rotation composition), q*=q, q*v (rotating v by q), q==q, q!=q.
Static attributes: Identity.
3-dimensional float vector.
Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.
Implicit conversion from sequence (list, tuple, ...) of 2 floats.
Static attributes: Zero, Ones, UnitX, UnitY.
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
/TODO/
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Mean value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
2-dimensional integer vector.
Supported operations (i if an int, v is a Vector2i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.
Implicit conversion from sequence (list, tuple, ...) of 2 integers.
Static attributes: Zero, Ones, UnitX, UnitY.
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Outer product with other.
Product of all elements.
Number of rows.
Sum of all elements.
3-dimensional float vector.
Supported operations (f if a float/int, v is a Vector3): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v, plus operations with Matrix3 and Quaternion.
Implicit conversion from sequence (list, tuple, ...) of 3 floats.
Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
/TODO/
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Mean value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
3-dimensional integer vector.
Supported operations (i if an int, v is a Vector3i): -v, v+v, v+=v, v-v, v-=v, v*i, i*v, v*=i, v==v, v!=v.
Implicit conversion from sequence (list, tuple, ...) of 3 integers.
Static attributes: Zero, Ones, UnitX, UnitY, UnitZ.
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Outer product with other.
Product of all elements.
Number of rows.
Sum of all elements.
6-dimensional float vector.
Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.
Implicit conversion from sequence (list, tuple, ...) of 6 floats.
Static attributes: Zero, Ones.
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
/TODO/
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Mean value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
6-dimensional float vector.
Supported operations (f if a float/int, v is a Vector6): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.
Implicit conversion from sequence (list, tuple, ...) of 6 floats.
Static attributes: Zero, Ones.
Return an object where all elements are randomly set to values between 0 and 1.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Outer product with other.
Product of all elements.
Number of rows.
Sum of all elements.
Dynamic-sized float vector.
Supported operations (f if a float/int, v is a VectorX): -v, v+v, v+=v, v-v, v-=v, v*f, f*v, v*=f, v/f, v/=f, v==v, v!=v.
Implicit conversion from sequence (list, tuple, ...) of X floats.
Return vector of given length with all elements set to values between 0 and 1 randomly.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Maximum value over all elements.
Mean value over all elements.
Minimum value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
/TODO/
Return vector of given length with all elements set to values between 0 and 1 randomly.
Return diagonal matrix with this vector on the diagonal.
Number of columns.
Dot product with other.
Approximate comparison with precision prec.
Maximum absolute value over all elements.
Mean value over all elements.
Euclidean norm.
Normalize this object in-place.
Return normalized copy of this object
Outer product with other.
Product of all elements.
Zero all elements which are greater than absTol. Negative zeros are not pruned.
Number of rows.
Square of the Euclidean norm.
Sum of all elements.
Return the shortest string representation of f which will is equal to f when converted back to float. This function is only useful in Python prior to 3.0; starting from that version, standard string conversion does just that.