// \file cmVectOpsTemplateHdr.h /// Vector operations interface. /// Setting fieldWidth or decPltCnt to to negative values result in fieldWidth == 10 or decPlCnt == 4 void VECT_OP_FUNC(Printf)( cmRpt_t* rpt, unsigned rn, unsigned cn, const VECT_OP_TYPE* dbp, unsigned fieldWidth, unsigned decPlCnt, const char* fmt, unsigned flags ); void VECT_OP_FUNC(Print)( cmRpt_t* rpt, unsigned rn, unsigned cn, const VECT_OP_TYPE* dbp ); void VECT_OP_FUNC(PrintE)( cmRpt_t* rpt, unsigned rn, unsigned cn, const VECT_OP_TYPE* dbp ); void VECT_OP_FUNC(PrintLf)( const char* label, cmRpt_t* rpt, unsigned rn, unsigned cn, const VECT_OP_TYPE* dbp, unsigned fieldWidth, unsigned decPlCnt, const char* fmt ); void VECT_OP_FUNC(PrintL)( const char* label, cmRpt_t* rpt, unsigned rn, unsigned cn, const VECT_OP_TYPE* dbp ); void VECT_OP_FUNC(PrintLE)( const char* label, cmRpt_t* rpt, unsigned rn, unsigned cn, const VECT_OP_TYPE* dbp ); /// Normalize the vector of proabilities by dividing through by the sum. /// This leaves the relative proportions of each value unchanged while producing a total probability of 1.0. VECT_OP_TYPE* VECT_OP_FUNC(NormalizeProbabilityVV)(VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp); VECT_OP_TYPE* VECT_OP_FUNC(NormalizeProbability)(VECT_OP_TYPE* dbp, unsigned dn); VECT_OP_TYPE* VECT_OP_FUNC(NormalizeProbabilityN)(VECT_OP_TYPE* dbp, unsigned dn, unsigned stride); /// Standardize the columns of the matrix by subtracting the mean and dividing by the standard deviation. /// uV[dcn] returns the mean of the data and is optional. /// sdV[dcn] return the standard deviation of the data and is optional. VECT_OP_TYPE* VECT_OP_FUNC(StandardizeRows)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, VECT_OP_TYPE* uV, VECT_OP_TYPE* sdV ); VECT_OP_TYPE* VECT_OP_FUNC(StandardizeCols)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, VECT_OP_TYPE* uV, VECT_OP_TYPE* sdV ); /// dbp[] = sbp<0 .* sbp /// Overlapping the source and dest is allowable as long as dbp <= sbp. VECT_OP_TYPE* VECT_OP_FUNC(HalfWaveRectify)(VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp ); /// Compute the cummulative sum of sbp[dn]. Equivalent to Matlab cumsum(). VECT_OP_TYPE* VECT_OP_FUNC(CumSum)(VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp ); VECT_OP_TYPE VECT_OP_FUNC(Mean)( const VECT_OP_TYPE* sp, unsigned sn ); VECT_OP_TYPE VECT_OP_FUNC(MeanN)( const VECT_OP_TYPE* sp, unsigned sn, unsigned stride ); // Take the mean of each column/row of a matrix. // Set 'dim' to 0 to return mean of columns else return mean of rows. VECT_OP_TYPE* VECT_OP_FUNC(MeanM)( VECT_OP_TYPE* dp, const VECT_OP_TYPE* sp, unsigned srn, unsigned scn, unsigned dim ); // Find the mean of the data points returned by srcFuncPtr(argPtr,i) and return it in dp[dim]. // 'dim' is both the size of dp[] and the length of each data point returned by srcFuncPtr(). // srcFuncPtr() will be called 'cnt' times but it may return NULL on some calls if the associated // data point should not be included in the mean calculation. VECT_OP_TYPE* VECT_OP_FUNC(Mean2)( VECT_OP_TYPE* dp, const VECT_OP_TYPE* (*srcFuncPtr)(void* arg, unsigned idx ), unsigned dim, unsigned cnt, void* argPtr ); // avgPtr is optional - set to NULL to compute the average VECT_OP_TYPE VECT_OP_FUNC(Variance)( const VECT_OP_TYPE* sp, unsigned sn, const VECT_OP_TYPE* avgPtr ); VECT_OP_TYPE VECT_OP_FUNC(VarianceN)(const VECT_OP_TYPE* sp, unsigned sn, unsigned stride, const VECT_OP_TYPE* avgPtr ); // Set dim=0 to return variance of columns otherwise return variance or rows. VECT_OP_TYPE* VECT_OP_FUNC(VarianceM)(VECT_OP_TYPE* dp, const VECT_OP_TYPE* sp, unsigned srn, unsigned scn, const VECT_OP_TYPE* avgPtr, unsigned dim ); // dp[] ./= max(dp). Returns the index of the max value. unsigned VECT_OP_FUNC(NormToMax)( VECT_OP_TYPE* dp, unsigned dn ); VECT_OP_TYPE VECT_OP_FUNC(AlphaNorm)(const VECT_OP_TYPE* sp, unsigned sn, VECT_OP_TYPE alpha ); // Calculate the sample covariance matrix from a set of Gaussian distributed multidimensional data. // sp[dn,scn] is the data set. // dn is the dimensionality of the data. // scn is the count of data points // up[dn] is an optional mean vector. If up == NULL then the mean of the data is calculated internally. // selIdxV[scn] can be used to select a subset of datapoints to process. // If selIdxV[] is non-NULL then only columns where selIdxV[i]==selKey will be processed. // // dp[dn,dn] = covar( sp[dn,scn], u[dn] ) void VECT_OP_FUNC(GaussCovariance)(VECT_OP_TYPE* dp, unsigned dn, const VECT_OP_TYPE* sp, unsigned scn, const VECT_OP_TYPE* up, const unsigned* selIdxV, unsigned selKey ); // Calculate the sample covariance matrix. // dp[ dn*dn ] - output matrix // dn - dimensionality of the data // srcFuncPtr - User defined function which is called to return a pointer to a data vector at index 'idx'. // The returned data vector must contain 'dn' elements. The function should return NULL // if the data point associated with 'idx' should not be included in the covariance calculation. // sn - count of data vectors // userPtr - User arg. passed to srcFuncPtr. // uV[ dn ] - mean of the data set (optional) // Note that this function computes the covariance matrix in 2 serial passes (1 if the mean vector is given) // through the 'sn' data points. // The result of this function are identical to the octave cov() function. void VECT_OP_FUNC(GaussCovariance2)(VECT_OP_TYPE* dp, unsigned dn, const VECT_OP_TYPE* (*srcFuncPtr)(void* userPtr, unsigned idx), unsigned sn, void* userPtr, const VECT_OP_TYPE* uV, const unsigned* selIdxV, unsigned selKey ); bool VECT_OP_FUNC(Equal)( const VECT_OP_TYPE* s0p, const VECT_OP_TYPE* s1p, unsigned sn ); // Returns true if all values are 'normal' according the the C macro 'isnormal'. // This function will return false if any of the values are zero. bool VECT_OP_FUNC(IsNormal)( const VECT_OP_TYPE* sp, unsigned sn ); // Returns true if all values are 'normal' or zero according the the C macro 'isnormal'. // This function accepts zeros as normal. bool VECT_OP_FUNC(IsNormalZ)(const VECT_OP_TYPE* sp, unsigned sn ); // Set dp[dn] to the indexes of the non-normal numbers in sp[dn]. // Returns the count of indexes stored in dp[]. unsigned VECT_OP_FUNC(FindNonNormal)( unsigned* dp, unsigned dn, const VECT_OP_TYPE* sp ); unsigned VECT_OP_FUNC(FindNonNormalZ)( unsigned* dp, unsigned dn, const VECT_OP_TYPE* sp ); /// Successive call to to ZeroCrossCount should preserve the value pointed to by delaySmpPtr. unsigned VECT_OP_FUNC(ZeroCrossCount)( const VECT_OP_TYPE* sp, unsigned n, VECT_OP_TYPE* delaySmpPtr); /// sn must be <= wndSmpCnt. If sn < wndSmpCnt then sp[sn] is treated as a /// a partially filled buffer padded with wndSmpCnt-sn zeros. /// rms = sqrt( sum(sp[1:sn] .* sp[1:sn]) / wndSmpCnt ) VECT_OP_TYPE VECT_OP_FUNC(RMS)( const VECT_OP_TYPE* sp, unsigned sn, unsigned wndSmpCnt ); /// This function handles the case were sn is not an integer multiple of /// wndSmpCnt or hopSmpCnt. In this case the function computes zero /// padded RMS values for windows which go past the end of sp[sn]. VECT_OP_TYPE* VECT_OP_FUNC(RmsV)( VECT_OP_TYPE* dp, unsigned dn, const VECT_OP_TYPE* sp, unsigned sn, unsigned wndSmpCnt, unsigned hopSmpCnt ); /// Return the magnitude (Euclidean Norm) of a vector. VECT_OP_TYPE VECT_OP_FUNC(EuclidNorm)( const VECT_OP_TYPE* sp, unsigned sn ); // Return the Itakura-Saito distance between a modelled power spectrum (up) and another power spectrum (sp). VECT_OP_TYPE VECT_OP_FUNC(ItakuraDistance)( const VECT_OP_TYPE* up, const VECT_OP_TYPE* sp, unsigned sn ); /// Return the cosine distance between two vectors. VECT_OP_TYPE VECT_OP_FUNC(CosineDistance)( const VECT_OP_TYPE* s0P, const VECT_OP_TYPE* s1p, unsigned sn ); /// Return the Euclidean distance between two vectors VECT_OP_TYPE VECT_OP_FUNC(EuclidDistance)( const VECT_OP_TYPE* s0p, const VECT_OP_TYPE* s1p, unsigned sn ); /// Return the Manhattan distance between two vectors VECT_OP_TYPE VECT_OP_FUNC(L1Distance)( const VECT_OP_TYPE* s0p, const VECT_OP_TYPE* s1p, unsigned sn ); /// Return the Mahalanobis distance between a vector and the mean of the distribution. /// The mean vector could be replaced with another vector drawn from the same distribution in which /// case the returned value would reflect the distance between the two vectors. /// 'sn' is the dimensionality of the data. /// up[D] and invCovM[sn,sn] are the mean and inverse of the covariance matrix of the distribution from /// which sp[D] is drawn. VECT_OP_TYPE VECT_OP_FUNC(MahalanobisDistance)( const VECT_OP_TYPE* sp, unsigned sn, const VECT_OP_TYPE* up, const VECT_OP_TYPE* invCovM ); /// Return the KL distance between two probability distributions up[sn] and sp[sn]. /// Since up[] and sp[] are probability distributions they must sum to 1.0. VECT_OP_TYPE VECT_OP_FUNC(KL_Distance)( const VECT_OP_TYPE* up, const VECT_OP_TYPE* sp, unsigned sn ); /// Return the KL distance between a prototype vector up[sn] and another vector sp[sn]. /// This function first normalizes the two vectors to sum to 1.0 before calling // VECT_OP_FUNC(KL_Distance)(up,sp,sn); VECT_OP_TYPE VECT_OP_FUNC(KL_Distance2)( const VECT_OP_TYPE* up, const VECT_OP_TYPE* sp, unsigned sn ); /// Measure the Euclidean distance between a vector and all the columns in a matrix. /// If dv[scn] is no NULL then return the Euclidean distance from sv[scn] to each column of sm[srn,scn]. /// The function returns the index of the closest data point (column) in sm[]. unsigned VECT_OP_FUNC(EuclidDistanceVM)( VECT_OP_TYPE* dv, const VECT_OP_TYPE* sv, const VECT_OP_TYPE* sm, unsigned srn, unsigned scn ); /// Measure the distance between each column in s0M[ rn, s0cn ] and /// each column in s1M[rn, s1cn ]. If dM is non-NULL store the /// result in dM[s1cn, s0cn]. The difference between s0M[:,0] and s1M[:,0] /// is stored in dM[0,0], the diff. between s0M[:,1] and s1M[:,1] is stored /// in dM[1,0], etc. If mvV[s0cn] is non-NULL then minV[i] is set with /// the distance from s0M[:,i] to the nearest column in s1M[]. If miV[s0cn] /// is non-NULL then it is set with the column index of s1M[] which is /// closest to s0M[:,i]. In other words mvV[i] gives the distance to column /// miV[i] from column s0M[:,i]. /// In those cases where the distane from a prototype (centroid) to the data point /// is not the same as from the data point to the centroid then s1M[] is considered /// to hold the prototypes and s0M[] is considered to hold the data points. /// The distance function returns the distance from a prototype 'cV[dimN]' to /// an datapoint dV[dimN]. 'dimN' is the dimensionality of the data vector /// and is threfore equal to 'rn'. void VECT_OP_FUNC(DistVMM)( VECT_OP_TYPE* dM, // dM[s1cn,s0cn] return distance mtx (optional) VECT_OP_TYPE* mvV, // mvV[s0cn] distance to closest data point in s0M[]. (optional) unsigned* miV, // miV[s0cn] column index into s1M[] of closest data point to s0M[:,i]. (optional) unsigned rn, // dimensionality of the data and the row count for s0M[] and s1M[] const VECT_OP_TYPE* s0M, // s0M[rn,s0cn] contains one data point per column unsigned s0cn, // count of data points (count of columns in s0M[] const VECT_OP_TYPE* s1M, // s1M[rn,s1cn] contains one prototype per column unsigned s1cn, // count of prototypes (count of columns in s1m[] VECT_OP_TYPE (*distFunc)( void* userPtr, const VECT_OP_TYPE* cV, const VECT_OP_TYPE* dV, unsigned dimN ), void* userPtr ); /// Select 'selIdxN' columns from sM[srn,scn]. /// dM[srn,selIdxN] receives copies of the selected columns. /// selIdxV[selIdxN] receives the column indexes of the selected columns. /// Both dM[] and selIdxV[] are optional. /// In each case the first selected point is chosen at random. /// SelectRandom() then selects the following selIdxN-1 points at random. /// SelectMaxDist() selects the next selIdxN-1 points by selecting /// the point whose combined distance to the previously selected points /// is greatest. SelectMaxAvgDist() selectes the points whose combined /// average distance is greatest relative the the previously selected /// points. void VECT_OP_FUNC(SelectRandom)( VECT_OP_TYPE* dM, unsigned* selIdxV, unsigned selIdxN, const VECT_OP_TYPE* sM, unsigned srn, unsigned scn ); void VECT_OP_FUNC(SelectMaxDist)( VECT_OP_TYPE* dM, unsigned* selIdxV, unsigned selIdxN, const VECT_OP_TYPE* sM, unsigned srn, unsigned scn, VECT_OP_TYPE (*distFunc)( void* userPtr, const VECT_OP_TYPE* s0V, const VECT_OP_TYPE* s1V, unsigned sn ), void* distUserPtr ); void VECT_OP_FUNC(SelectMaxAvgDist)( VECT_OP_TYPE* dM, unsigned* selIdxV, unsigned selIdxN, const VECT_OP_TYPE* sM, unsigned srn, unsigned scn, VECT_OP_TYPE (*distFunc)( void* userPtr, const VECT_OP_TYPE* s0V, const VECT_OP_TYPE* s1V, unsigned sn ), void* distUserPtr ); /// Return the sum of the products (dot product) VECT_OP_TYPE VECT_OP_FUNC(MultSumVV)( const VECT_OP_TYPE* s0p, const VECT_OP_TYPE* s1p, unsigned sn ); VECT_OP_TYPE VECT_OP_FUNC(MultSumVS)( const VECT_OP_TYPE* s0p, unsigned sn, VECT_OP_TYPE s ); /// Number of elements in the dest vector is expected to be the same /// as the number of source matrix rows. /// mcn gives the number of columns in the source matrix which is // expected to match the number of elements in the source vector. /// dbp[dn,1] = mp[dn,mcn] * vp[mcn,1] VECT_OP_TYPE* VECT_OP_FUNC(MultVMV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* mp, unsigned mcn, const VECT_OP_TYPE* vp ); /// Multiply a row vector with a matrix to produce a row vector. /// dbp[1,dn] = v[1,vn] * m[vn,dn] VECT_OP_TYPE* VECT_OP_FUNC(MultVVM)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* vp, unsigned vn, const VECT_OP_TYPE* mp ); /// Same as MultVMtV() except M is transposed as part of the multiply. /// mrn gives the number of rows in m[] and number of elements in vp[] /// dpb[dn] = mp[mrn,dn] * vp[mrn] VECT_OP_TYPE* VECT_OP_FUNC(MultVMtV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* mp, unsigned mrn, const VECT_OP_TYPE* vp ); /// Same as MultVMV() but where the matrix is diagonal. VECT_OP_TYPE* VECT_OP_FUNC(MultDiagVMV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* mp, unsigned mcn, const VECT_OP_TYPE* vp ); /// Generalized matrix multiply. /// If transposition is selected for M0 or M1 then the given dimension represent the size of the matrix 'after' the transposion. /// d[drn,dcn] = alpha * op(m0[drn,m0cn_m1rn]) * op(m1[m0cn_m1rn,dcn]) + beta * d[drn,dcn] //// See enum { kTranpsoseM0Fl=0x01, kTransposeM1Fl=0x02 } in cmVectOps for flags. VECT_OP_TYPE* VECT_OP_FUNC(MultMMM1)(VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, VECT_OP_TYPE alpha, const VECT_OP_TYPE* m0, const VECT_OP_TYPE* m1, unsigned m0cn_m1rn, VECT_OP_TYPE beta, unsigned flags ); /// Same a VECT_OP_FUNC(MultMMM1) except allows the operation on a sub-matrix by providing the physical (memory) row count rather than the logical (matrix) row count. VECT_OP_TYPE* VECT_OP_FUNC(MultMMM2)(VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, VECT_OP_TYPE alpha, const VECT_OP_TYPE* m0, const VECT_OP_TYPE* m1, unsigned m0cn_m1rn, VECT_OP_TYPE beta, unsigned flags, unsigned dprn, unsigned m0prn, unsigned m1prn ); /// d[drn,dcn] = m0[drn,m0cn] * m1[m1rn,dcn] VECT_OP_TYPE* VECT_OP_FUNC(MultMMM)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, const VECT_OP_TYPE* m0, const VECT_OP_TYPE* m1, unsigned m0cn_m1rn ); /// same as MultMMM() except second source matrix is transposed prior to the multiply VECT_OP_TYPE* VECT_OP_FUNC(MultMMMt)(VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, const VECT_OP_TYPE* m0, const VECT_OP_TYPE* m1, unsigned m0cn_m1rn ); // Raise dbp[] to the power 'expon' VECT_OP_TYPE* VECT_OP_FUNC(PowVS)( VECT_OP_TYPE* dbp, unsigned dn, VECT_OP_TYPE expon ); VECT_OP_TYPE* VECT_OP_FUNC(PowVVS)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp, VECT_OP_TYPE expon ); // Take the natural log of all values in sbp[dn]. It is allowable for sbp point to the same array as dbp=. VECT_OP_TYPE* VECT_OP_FUNC(LogV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp ); // Convert a magnitude (amplitude) spectrum to/from decibels. // It is allowable for dbp==sbp. VECT_OP_TYPE* VECT_OP_FUNC(AmplToDbVV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp, VECT_OP_TYPE minDb ); VECT_OP_TYPE* VECT_OP_FUNC(DbToAmplVV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp); VECT_OP_TYPE* VECT_OP_FUNC(PowToDbVV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp, VECT_OP_TYPE minDb ); VECT_OP_TYPE* VECT_OP_FUNC(DbToPowVV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp); /// Initialize dbp[dn,dn] as a square symetric positive definite matrix using values /// from a random uniform distribution. This is useful for initializing random /// covariance matrices as used by multivariate Gaussian distributions /// If t is non-NULL it must point to a block of scratch memory of t[dn,dn]. /// If t is NULL then scratch memory is internally allocated and deallocated. VECT_OP_TYPE* VECT_OP_FUNC(RandSymPosDef)( VECT_OP_TYPE* dbp, unsigned dn, VECT_OP_TYPE* t ); /// Compute the determinant of any square matrix. VECT_OP_TYPE VECT_OP_FUNC(DetM)( const VECT_OP_TYPE* sp, unsigned srn ); /// Compute the determinant of a diagonal matrix. VECT_OP_TYPE VECT_OP_FUNC(DetDiagM)( const VECT_OP_TYPE* sp, unsigned srn); /// Compute the log determinant of any square matrix. VECT_OP_TYPE VECT_OP_FUNC(LogDetM)( const VECT_OP_TYPE* sp, unsigned srn ); /// Compute the log determinant of a diagonal matrix. VECT_OP_TYPE VECT_OP_FUNC(LogDetDiagM)( const VECT_OP_TYPE* sp, unsigned srn); /// Compute the inverse of a square matrix. Returns NULL if the matrix is not invertable. /// 'drn' is the dimensionality of the data. VECT_OP_TYPE* VECT_OP_FUNC(InvM)( VECT_OP_TYPE* dp, unsigned drn ); /// Compute the inverse of a diagonal matrix. Returns NULL if the matrix is not invertable. VECT_OP_TYPE* VECT_OP_FUNC(InvDiagM)( VECT_OP_TYPE* dp, unsigned drn ); /// Solve a linear system of the form AX=B where A[an,an] is square. /// Since A is square B must have 'an' rows. /// Result is returned in B. /// Returns a pointer to B on success or NULL on fail. /// NOTE: Both A and B are overwritten by this operation. VECT_OP_TYPE* VECT_OP_FUNC(SolveLS)( VECT_OP_TYPE* A, unsigned an, VECT_OP_TYPE* B, unsigned bcn ); /// Perform a Cholesky decomposition of the square symetric matrix U[un,un]. /// The factorization has the form: A=U'TU. /// If the factorization is successful A is set to U and a pointer to A is returned. /// Note that the lower triangle of A is not overwritten. See CholZ(). /// If the factorization fails NULL is returned. VECT_OP_TYPE* VECT_OP_FUNC(Chol)(VECT_OP_TYPE* A, unsigned an ); /// Same as Chol() but sets the lower triangle of U to zero. /// This is equivalent ot the Matlab version. VECT_OP_TYPE* VECT_OP_FUNC(CholZ)(VECT_OP_TYPE* U, unsigned un ); /// Return the average value of the contents of sbp[] between two fractional indexes VECT_OP_TYPE VECT_OP_FUNC(FracAvg)( double bi, double ei, const VECT_OP_TYPE* sbp, unsigned sn ); /// Shrinking function - Decrease the size of sbp[] by averaging blocks of values into single values in dbp[] VECT_OP_TYPE* VECT_OP_FUNC(DownSampleAvg)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp, unsigned sn ); /// Stretching function - linear interpolate between points in sbp[] to fill dbp[] ... where dn > sn VECT_OP_TYPE* VECT_OP_FUNC(UpSampleInterp)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp, unsigned sn ); /// Stretch or shrink the sbp[] to fit into dbp[] VECT_OP_TYPE* VECT_OP_FUNC(FitToSize)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp, unsigned sn ); /// Stretch or shrink sV[] to fit into dV[] using a simple linear mapping. /// When stretching (sndn) each dest value is formed by the average of sequential segments /// of sn/dn source elements. Fractional values are used at the beginning /// and end of each segment. VECT_OP_TYPE* VECT_OP_FUNC(LinearMap)(VECT_OP_TYPE* dV, unsigned dn, VECT_OP_TYPE* sV, unsigned sn ); /// Generate a vector of uniformly distributed random numbers in the range minVal to maxVal. VECT_OP_TYPE* VECT_OP_FUNC(Random)( VECT_OP_TYPE* dbp, unsigned dn, VECT_OP_TYPE minVal, VECT_OP_TYPE maxVal ); /// Generate dn random numbers integers between 0 and wn-1 based on a the relative /// weights in wp[wn]. Note thtat the weights do not have to sum to 1.0. unsigned* VECT_OP_FUNC(WeightedRandInt)( unsigned* dbp, unsigned dn, const VECT_OP_TYPE* wp, unsigned wn ); /// Generate a vector of normally distributed univariate random numbers VECT_OP_TYPE* VECT_OP_FUNC(RandomGauss)( VECT_OP_TYPE* dbp, unsigned dn, VECT_OP_TYPE mean, VECT_OP_TYPE var ); /// Generate a vector of normally distributed univariate random numbers where each value has been drawn from a /// seperately parameterized Gaussian distribution. meanV[] and varV[] must both contain dn velues. VECT_OP_TYPE* VECT_OP_FUNC(RandomGaussV)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* varV ); /// Generate a matrix of multi-dimensional random values. Each column represents a single vector value and each row contains a dimension. /// meanV[] and varV[] must both contain drn elements where each meanV[i],varV[i] pair parameterize one dimensions Gaussian distribution. VECT_OP_TYPE* VECT_OP_FUNC(RandomGaussM)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* varV ); VECT_OP_TYPE* VECT_OP_FUNC(RandomGaussDiagM)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* diagCovarM ); /// Generate a matrix of multivariate random values drawn from a normal distribution. /// The dimensionality of the values are 'drn'. /// The count of returned values is 'dcn'. /// meanV[drn] and covarM[drn,drn] parameterize the normal distribution. /// The covariance matrix must be symetric and positive definite. /// t[(drn*drn) ] points to scratch memory or is set to NULL if the function should /// allocate the memory internally. /// Based on octave function mvrnd.m. VECT_OP_TYPE* VECT_OP_FUNC(RandomGaussNonDiagM)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* covarM, VECT_OP_TYPE* t ); /// Same as RandomGaussNonDiagM() except requires the upper trianglular /// Cholesky factor of the covar matrix in 'uM'. VECT_OP_TYPE* VECT_OP_FUNC(RandomGaussNonDiagM2)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* uM ); /// Generate a matrix of N*K multi-dimensional data points. /// Where D is the dimensionality of the data. (D == drn). /// K is the number of multi-dimensional PDF's (clusters). /// N is the number of data points to generate per cluster. /// dbp[ D, N*K ] contains the returned data point. /// The first N columns is associated with the cluster 0, /// the next N columns is associated with cluster 1, ... /// meanM[ D, K ] and varM[D,K] parameterize the generating PDF.s for each cluster VECT_OP_TYPE* VECT_OP_FUNC(RandomGaussMM)( VECT_OP_TYPE* dbp, unsigned drn, unsigned dcn, const VECT_OP_TYPE* meanM, const VECT_OP_TYPE* varM, unsigned K ); /// Generate the set of coordinates which describe a circle with a center at x,y. /// dbp[dn,2] must contain 2*dn elements. The first column holds the x coord and and the second holds the y coord. VECT_OP_TYPE* VECT_OP_FUNC(CircleCoords)( VECT_OP_TYPE* dbp, unsigned dn, VECT_OP_TYPE x, VECT_OP_TYPE y, VECT_OP_TYPE varX, VECT_OP_TYPE varY ); /// The following functions all return the phase of the next value. unsigned VECT_OP_FUNC(SynthSine)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz ); unsigned VECT_OP_FUNC(SynthCosine)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz ); unsigned VECT_OP_FUNC(SynthSquare)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz, unsigned otCnt ); unsigned VECT_OP_FUNC(SynthTriangle)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz, unsigned otCnt ); unsigned VECT_OP_FUNC(SynthSawtooth)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz, unsigned otCnt ); unsigned VECT_OP_FUNC(SynthPulseCos)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz, unsigned otCnt ); unsigned VECT_OP_FUNC(SynthImpulse)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz ); unsigned VECT_OP_FUNC(SynthPhasor)( VECT_OP_TYPE* dbp, unsigned dn, unsigned phase, double srate, double hz ); /// Return value should be passed back via delaySmp on the next call. VECT_OP_TYPE VECT_OP_FUNC(SynthPinkNoise)( VECT_OP_TYPE* dbp, unsigned dn, VECT_OP_TYPE delaySmp ); VECT_OP_TYPE* VECT_OP_FUNC(LinearToDb)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp, VECT_OP_TYPE mult ); VECT_OP_TYPE* VECT_OP_FUNC(dBToLinear)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp, VECT_OP_TYPE mult ); VECT_OP_TYPE* VECT_OP_FUNC(AmplitudeToDb)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp ); VECT_OP_TYPE* VECT_OP_FUNC(PowerToDb)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp ); VECT_OP_TYPE* VECT_OP_FUNC(dBToAmplitude)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp ); VECT_OP_TYPE* VECT_OP_FUNC(dBToPower)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sp ); VECT_OP_TYPE VECT_OP_FUNC(KaiserBetaFromSidelobeReject)( double sidelobeRejectDb ); VECT_OP_TYPE VECT_OP_FUNC(KaiserFreqResolutionFactor)( double sidelobeRejectDb ); VECT_OP_TYPE* VECT_OP_FUNC(Kaiser)( VECT_OP_TYPE* dbp, unsigned dn, double beta ); VECT_OP_TYPE* VECT_OP_FUNC(Gaussian)(VECT_OP_TYPE* dbp, unsigned dn, double mean, double variance ); VECT_OP_TYPE* VECT_OP_FUNC(Hamming)( VECT_OP_TYPE* dbp, unsigned dn ); VECT_OP_TYPE* VECT_OP_FUNC(Hann)( VECT_OP_TYPE* dbp, unsigned dn ); VECT_OP_TYPE* VECT_OP_FUNC(Triangle)(VECT_OP_TYPE* dbp, unsigned dn ); /// The MATLAB equivalent Hamming and Hann windows. //VECT_OP_TYPE* VECT_OP_FUNC(HammingMatlab)(VECT_OP_TYPE* dbp, unsigned dn ); VECT_OP_TYPE* VECT_OP_FUNC(HannMatlab)( VECT_OP_TYPE* dbp, unsigned dn ); /// Simulates the MATLAB GaussWin function. Set arg to 2.5 to simulate the default arg /// as used by MATLAB. VECT_OP_TYPE* VECT_OP_FUNC(GaussWin)( VECT_OP_TYPE* dbp, unsigned dn, double arg ); /// Direct form II algorithm based on the MATLAB implmentation /// http://www.mathworks.com/access/helpdesk/help/techdoc/ref/filter.html#f83-1015962 /// The only difference between this function and the equivalent MATLAB filter() function /// is that the first feedforward coeff is given as a seperate value. The first b coefficient /// in this function is therefore the same as the second coefficient in the MATLAB function. /// and the first a[] coefficient (which is generally set to 1.0) is skipped. /// Example: /// Matlab: b=[.5 .4 .3] a=[1 .2 .1] /// Equiv: b0 = .5 b=[ .4 .3] a=[ .2 .1]; /// /// y[yn] - output vector /// x[xn] - input vector. xn must be <= yn. if xn < yn then the end of y[] is set to zero. /// b0 - signal scale. This can also be seen as b[0] (which is not included in b[]) /// b[dn] - feedforward coeff's b[1..dn-1] /// a[dn] - feedback coeff's a[1..dn-1] /// d[dn+1] - delay registers - note that this array must be one element longer than the coeff arrays. /// VECT_OP_TYPE* VECT_OP_FUNC(Filter)( VECT_OP_TYPE* y, unsigned yn, const VECT_OP_TYPE* x, unsigned xn, cmReal_t b0, const cmReal_t* b, const cmReal_t* a, cmReal_t* d, unsigned dn ); struct cmFilter_str; //typedef cmRC_t (*VECT_OP_FUNC(FiltExecFunc_t))( struct acFilter_str* f, const VECT_OP_TYPE* x, unsigned xn, VECT_OP_TYPE* y, unsigned yn ); VECT_OP_TYPE* VECT_OP_FUNC(FilterFilter)(struct cmFilter_str* f, cmRC_t (*func)( struct cmFilter_str* f, const VECT_OP_TYPE* x, unsigned xn, VECT_OP_TYPE* y, unsigned yn ), const cmReal_t bb[], unsigned bn, const cmReal_t aa[], unsigned an, const VECT_OP_TYPE* x, unsigned xn, VECT_OP_TYPE* y, unsigned yn ); /// Compute the coefficients of a low/high pass FIR filter /// See enum { kHighPass_LPSincFl=0x01, kNormalize_LPSincFl=0x02 } in acVectOps.h VECT_OP_TYPE* VECT_OP_FUNC(LP_Sinc)(VECT_OP_TYPE* dp, unsigned dn, double srate, double fcHz, unsigned flags ); /// Compute the complex transient detection function from successive spectral frames. /// The spectral magntidue mag0V precedes mag1V and the phase (radians) spectrum phs0V precedes the phs1V which precedes phs2V. /// binCnt gives the length of each of the spectral vectors. VECT_OP_TYPE VECT_OP_FUNC(ComplexDetect)(const VECT_OP_TYPE* mag0V, const VECT_OP_TYPE* mag1V, const VECT_OP_TYPE* phs0V, const VECT_OP_TYPE* phs1V, const VECT_OP_TYPE* phs2V, unsigned binCnt ); /// Compute a set of filterCnt mel filter masks for wieghting magnitude spectra consisting of binCnt bins. /// The spectrum is divided into bandCnt equal bands in the mel domain /// Each row of the matrix contains the mask for a single filter band consisting of binCnt elements. /// See enum{ kShiftMelFl=0x01, kNormalizeMelFl=0x02 } in cmVectOps.h /// Set kShiftMelFl to shift the mel bands onto the nearest FFT bin. /// Set kNormalizeMelFl to normalize the combined filters for unity gain. VECT_OP_TYPE* VECT_OP_FUNC(MelMask)( VECT_OP_TYPE* maskMtx, unsigned bandCnt, unsigned binCnt, double srate, unsigned flags ); /// Fill binIdxV[bandCnt] and cntV[bandCnt] with a bin to band map. /// binIdx[] contains the first (minimum) bin index for a given band. /// cntV[] contains the count of bins for each band. /// bandCnt is the number of bark bands to return /// The function returns the actual number of bands mapped which will always be <= 23. unsigned VECT_OP_FUNC(BarkMap)(unsigned* binIdxV, unsigned* cntV, unsigned bandCnt, unsigned binCnt, double srate ); /// Calc a set of triangle fitler masks into each row of maskMtx. /// maskMtx[ bandCnt, binCnt ] - result matrix /// binHz - freq resolution of the output filters. /// stSpread - Semi-tone spread above and below each center frequency (stSpread*2) is the total bandwidth. /// (Only used if /// lowHz[ bandCnt ] - set of upper frequency limits for each band. /// ctrHz[ bandCnt ] set to the center value in Hz for each band /// uprHz[ bandCnt ] - set of lower frequency limits for each band. /// Note if lowHz[] and uprHz[] are set to NULL then stSpread is used to set the bandwidth of each band. VECT_OP_TYPE* VECT_OP_FUNC(TriangleMask)(VECT_OP_TYPE* maskMtx, unsigned bandCnt, unsigned binCnt, const VECT_OP_TYPE* ctrHzV, VECT_OP_TYPE binHz, VECT_OP_TYPE stSpread, const VECT_OP_TYPE* lowHzV, const VECT_OP_TYPE* uprHzV ); /// Calculate a set of Bark band triangle filters into maskMtx. /// Each row of maskMtx contains the filter for one band. /// maskMtx[ bandCnt, binCnt ] /// bandCnt - the number of triangle bankds. If bandCnt is > 24 it will be reduced to 24. /// binCnt - the number of bins in the filters. /// binHz - the width of each bin in Hz. VECT_OP_TYPE* VECT_OP_FUNC(BarkMask)(VECT_OP_TYPE* maskMtx, unsigned bandCnt, unsigned binCnt, double binHz ); // Terhardt 1979 (Calculating virtual pitch, Hearing Research #1, pp 155-182) // See enum { kNoTtmFlags=0, kModifiedTtmFl=0x01 } in cmVectOps.h VECT_OP_TYPE* VECT_OP_FUNC(TerhardtThresholdMask)(VECT_OP_TYPE* maskV, unsigned binCnt, double srate, unsigned flags); //Schroeder et al., 1979, JASA, Optimizing digital speech coders by exploiting masking properties of the human ear VECT_OP_TYPE* VECT_OP_FUNC(ShroederSpreadingFunc)(VECT_OP_TYPE* m, unsigned bandCnt, double srate); /// Compute a set of DCT-II coefficients. Result dp[ coeffCnt, filtCnt ] VECT_OP_TYPE* VECT_OP_FUNC(DctMatrix)( VECT_OP_TYPE* dp, unsigned coeffCnt, unsigned filtCnt ); /// Set the indexes of local peaks greater than threshold in dbp[]. /// Returns the number of peaks in dbp[] /// The maximum number of peaks from n source values is max(0,floor((n-1)/2)). /// Note that peaks will never be found at index 0 or index sn-1. unsigned VECT_OP_FUNC(PeakIndexes)( unsigned* dbp, unsigned dn, const VECT_OP_TYPE* sbp, unsigned sn, VECT_OP_TYPE threshold ); /// Return the index of the bin containing v or acInvalidIdx if v is below sbp[0] or above sbp[ n-1 ] /// The bin limits are contained in sbp[]. /// The value in spb[] are therefore expected to be in increasing order. /// The value returned will be in the range 0:sn-1. unsigned VECT_OP_FUNC(BinIndex)( const VECT_OP_TYPE* sbp, unsigned sn, VECT_OP_TYPE v ); /// Assign each data point to one of k clusters using an expectation-maximization algorithm. /// k gives the number of clusters to identify /// Each column of sp[ srn, scn ] contains a multidimensional data point. /// srn therefore defines the dimensionality of the data. /// Each column of centroidV[ srn, k ] is set to the centroid of each of k clusters. /// classIdxV[ scn ] assigns the index (0 to k-1) of a cluster to each soure data point /// The function returns the number of iterations required for the EM process to converge. /// selIdxV[ scn ] is optional and contains a list of id's assoc'd with each column of sM. /// selKey is a integer value. /// If selIdxV is non-NULL then only columns of sM[] where selIdxV[] == selKey will be clustered. /// All columns of sM[] where the associated column in selIdxV[] do not match will be ignored. /// Set 'initFromCentroidFl' to true if the initial centroids should be taken from centroidM[]. /// otherwise the initial centroids are selected from 'k' random data points in sp[]. /// The distance function distFunc(cV,dV,dN) is called to determine the distance from a /// centroid the centroid 'cV[dN]' to a data point 'dV[dN]'. 'dN' is the dimensionality of the /// feature vector and is therefore equal to 'srn'. unsigned VECT_OP_FUNC(Kmeans)( unsigned* classIdxV, VECT_OP_TYPE* centroidM, unsigned k, const VECT_OP_TYPE* sp, unsigned srn, unsigned scn, const unsigned* selIdxV, unsigned selKey, bool initFromCentroidFl, VECT_OP_TYPE (*distFunc)( void* userPtr, const VECT_OP_TYPE* cV, const VECT_OP_TYPE* dV, unsigned dN ), void* userDistPtr ); /// 'srcFunc() should return NULL if the data point located at 'frmIdx' should not be included in the clustering. /// Clustering is considered to be complete after 'maxIterCnt' iterations or when /// 'deltaStopCnt' or fewer data points change class on a single iteration unsigned VECT_OP_FUNC(Kmeans2)( unsigned* classIdxV, // classIdxV[scn] - data point class assignments VECT_OP_TYPE* centroidM, // centroidM[srn,K] - cluster centroids unsigned K, // count of clusters const VECT_OP_TYPE* (*srcFunc)(void* userPtr, unsigned frmIdx ), unsigned srn, // dimensionality of each data point unsigned scn, // count of data points void* userSrcPtr, // callback data for srcFunc VECT_OP_TYPE (*distFunc)( void* userPtr, const VECT_OP_TYPE* cV, const VECT_OP_TYPE* dV, unsigned dN ), void* userDistPtr, // arg. to distFunc() int iterCnt, // max. number of iterations (-1 to ignore) int deltaStopCnt); // if less than deltaStopCnt data points change classes on a given iteration then convergence occurs. /// Evaluate the univariate normal distribution defined by 'mean' and 'stdDev'. VECT_OP_TYPE* VECT_OP_FUNC(GaussPDF)( VECT_OP_TYPE* dbp, unsigned dn, const VECT_OP_TYPE* sbp, VECT_OP_TYPE mean, VECT_OP_TYPE stdDev ); /// Evaluate a multivariate normal distribution defined by meanV[D] and covarM[D,D] /// at the data points held in the columns of xM[D,N]. Return the evaluation /// results in the vector yV[N]. D is the dimensionality of the data. N is the number of /// data points to evaluate and values to return in yV[N]. /// Set diagFl to true if covarM is diagonal. /// The function fails and returns false if the covariance matrix is singular. bool VECT_OP_FUNC(MultVarGaussPDF)( VECT_OP_TYPE* yV, const VECT_OP_TYPE* xM, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* covarM, unsigned D, unsigned N, bool diagFl ); /// Same as multVarGaussPDF[] except takes the inverse covar mtx invCovarM[D,D] /// and log determinant of covar mtx. /// Always returns yV[]. VECT_OP_TYPE* VECT_OP_FUNC(MultVarGaussPDF2)( VECT_OP_TYPE* yV, const VECT_OP_TYPE* xM, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* invCovarM, VECT_OP_TYPE logDet, unsigned D, unsigned N, bool diagFl ); /// Same as multVarGaussPDF[] except uses a function to obtain the data vectors. /// srcFunc() can filter the data points by returning NULL if the data vector at frmIdx should /// not be evaluated against the PDF. In this case yV[frmIdx] will be set to 0. VECT_OP_TYPE* VECT_OP_FUNC(MultVarGaussPDF3)( VECT_OP_TYPE* yV, const VECT_OP_TYPE* (*srcFunc)(void* funcDataPtr, unsigned frmIdx ), void* funcDataPtr, const VECT_OP_TYPE* meanV, const VECT_OP_TYPE* invCovarM, VECT_OP_TYPE logDet, unsigned D, unsigned N, bool diagFl ); /// Determine the most likely state sequece stateV[timeN] given a /// transition matrix a[stateN,stateN], /// observation probability matrix b[stateN,timeN] and /// initial state probability vector phi[stateN]. /// a[i,j] is the probability of transitioning from state i to state j. /// b[i,t] is the probability of state i emitting the obj t. void VECT_OP_FUNC(DiscreteViterbi)(unsigned* stateV, unsigned timeN, unsigned stateN, const VECT_OP_TYPE* phi, const VECT_OP_TYPE* a, const VECT_OP_TYPE* b ); /// Clip the line defined by x0,y0 to x1,y1 into the rect defined by xMin,yMin xMax,yMax. bool VECT_OP_FUNC(ClipLine)( VECT_OP_TYPE* x0, VECT_OP_TYPE* y0, VECT_OP_TYPE* x1, VECT_OP_TYPE* y1, VECT_OP_TYPE xMin, VECT_OP_TYPE yMin, VECT_OP_TYPE xMax, VECT_OP_TYPE yMax ); /// Return true if the line defined by x0,y0 to x1,y1 intersects with /// the rectangle formed by xMin,yMin - xMax,yMax bool VECT_OP_FUNC(IsLineInRect)( VECT_OP_TYPE x0, VECT_OP_TYPE y0, VECT_OP_TYPE x1, VECT_OP_TYPE y1, VECT_OP_TYPE xMin, VECT_OP_TYPE yMin, VECT_OP_TYPE xMax, VECT_OP_TYPE yMax ); /// Return the perpendicular distance from the line formed by x0,y0 and x1,y1 /// and the point px,py VECT_OP_TYPE VECT_OP_FUNC(PtToLineDistance)( VECT_OP_TYPE x0, VECT_OP_TYPE y0, VECT_OP_TYPE x1, VECT_OP_TYPE y1, VECT_OP_TYPE px, VECT_OP_TYPE py); /// Calculate the best fit line: b0 + b1*x_i through the points x_i,y_i. /// Set x to NULL if it uses sequential integers [0,1,2,3...] void VECT_OP_FUNC(Lsq1)(const VECT_OP_TYPE* x, const VECT_OP_TYPE* y, unsigned n, VECT_OP_TYPE* b0, VECT_OP_TYPE* b1 );