mirror of
https://gitlab.com/libeigen/eigen.git
synced 2025-04-22 01:29:35 +08:00
Merged eigen/eigen into default
This commit is contained in:
Rasmus Larsen
commit
5a59452aae
@ -216,7 +216,7 @@ pandnot(const Packet& a, const Packet& b) { return a & (~b); }
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/** \internal \returns ones */
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template<typename Packet> EIGEN_DEVICE_FUNC inline Packet
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ptrue(const Packet& /*a*/) { Packet b; memset(&b, 0xff, sizeof(b)); return b;}
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ptrue(const Packet& /*a*/) { Packet b; memset((void*)&b, 0xff, sizeof(b)); return b;}
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template <typename RealScalar>
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EIGEN_DEVICE_FUNC inline std::complex<RealScalar> ptrue(const std::complex<RealScalar>& /*a*/) {
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@ -308,18 +308,18 @@ template<> EIGEN_STRONG_INLINE void pstoreu<std::complex<double> >(std::complex<
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template<> EIGEN_DEVICE_FUNC inline Packet4cd pgather<std::complex<double>, Packet4cd>(const std::complex<double>* from, Index stride)
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{
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return Packet4cd(_mm512_insertf64x4(_mm512_castpd256_pd512(
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_mm256_insertf128_pd(_mm256_castpd128_pd256(pload<Packet1cd>(from+0*stride).v), pload<Packet1cd>(from+1*stride).v,1)),
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_mm256_insertf128_pd(_mm256_castpd128_pd256(pload<Packet1cd>(from+2*stride).v), pload<Packet1cd>(from+3*stride).v,1), 1));
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_mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+0*stride).v), ploadu<Packet1cd>(from+1*stride).v,1)),
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_mm256_insertf128_pd(_mm256_castpd128_pd256(ploadu<Packet1cd>(from+2*stride).v), ploadu<Packet1cd>(from+3*stride).v,1), 1));
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}
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template<> EIGEN_DEVICE_FUNC inline void pscatter<std::complex<double>, Packet4cd>(std::complex<double>* to, const Packet4cd& from, Index stride)
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{
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__m512i fromi = _mm512_castpd_si512(from.v);
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double* tod = (double*)(void*)to;
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_mm_store_pd(tod+0*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,0)) );
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_mm_store_pd(tod+2*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,1)) );
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_mm_store_pd(tod+4*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,2)) );
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_mm_store_pd(tod+6*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,3)) );
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_mm_storeu_pd(tod+0*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,0)) );
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_mm_storeu_pd(tod+2*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,1)) );
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_mm_storeu_pd(tod+4*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,2)) );
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_mm_storeu_pd(tod+6*stride, _mm_castsi128_pd(_mm512_extracti32x4_epi32(fromi,3)) );
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}
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template<> EIGEN_STRONG_INLINE std::complex<double> pfirst<Packet4cd>(const Packet4cd& a)
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@ -57,7 +57,7 @@ template<> struct packet_traits<float> : default_packet_traits
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HasBlend = 0,
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HasSin = EIGEN_FAST_MATH,
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HasCos = EIGEN_FAST_MATH,
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#if EIGEN_GNUC_AT_LEAST(5, 3) || EIGEN_COMP_CLANG
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#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
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#ifdef EIGEN_VECTORIZE_AVX512DQ
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HasLog = 1,
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#endif
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@ -77,7 +77,7 @@ template<> struct packet_traits<double> : default_packet_traits
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AlignedOnScalar = 1,
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size = 8,
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HasHalfPacket = 1,
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#if EIGEN_GNUC_AT_LEAST(5, 3)
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#if EIGEN_GNUC_AT_LEAST(5, 3) || (!EIGEN_COMP_GNUC_STRICT)
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HasSqrt = EIGEN_FAST_MATH,
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HasRsqrt = EIGEN_FAST_MATH,
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#endif
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@ -3,7 +3,7 @@
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//
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// Copyright (C) 2007 Julien Pommier
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// Copyright (C) 2014 Pedro Gonnet (pedro.gonnet@gmail.com)
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// Copyright (C) 2009-2018 Gael Guennebaud <gael.guennebaud@inria.fr>
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// Copyright (C) 2009-2019 Gael Guennebaud <gael.guennebaud@inria.fr>
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//
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// This Source Code Form is subject to the terms of the Mozilla
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// Public License v. 2.0. If a copy of the MPL was not distributed
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@ -253,15 +253,68 @@ Packet pexp_double(const Packet _x)
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return pmax(pldexp(x,fx), _x);
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}
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/* The code is the rewriting of the cephes sinf/cosf functions.
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Precision is excellent as long as x < 8192 (I did not bother to
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take into account the special handling they have for greater values
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-- it does not return garbage for arguments over 8192, though, but
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the extra precision is missing).
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// The following code is inspired by the following stack-overflow answer:
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// https://stackoverflow.com/questions/30463616/payne-hanek-algorithm-implementation-in-c/30465751#30465751
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// It has been largely optimized:
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// - By-pass calls to frexp.
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// - Aligned loads of required 96 bits of 2/pi. This is accomplished by
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// (1) balancing the mantissa and exponent to the required bits of 2/pi are
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// aligned on 8-bits, and (2) replicating the storage of the bits of 2/pi.
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// - Avoid a branch in rounding and extraction of the remaining fractional part.
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// Overall, I measured a speed up higher than x2 on x86-64.
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inline float trig_reduce_huge (float xf, int *quadrant)
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{
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using Eigen::numext::int32_t;
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using Eigen::numext::uint32_t;
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using Eigen::numext::int64_t;
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using Eigen::numext::uint64_t;
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Note that it is such that sinf((float)M_PI) = 8.74e-8, which is the
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surprising but correct result.
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*/
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const double pio2_62 = 3.4061215800865545e-19; // pi/2 * 2^-62
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const uint64_t zero_dot_five = uint64_t(1) << 61; // 0.5 in 2.62-bit fixed-point foramt
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// 192 bits of 2/pi for Payne-Hanek reduction
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// Bits are introduced by packet of 8 to enable aligned reads.
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static const uint32_t two_over_pi [] =
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{
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0x00000028, 0x000028be, 0x0028be60, 0x28be60db,
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0xbe60db93, 0x60db9391, 0xdb939105, 0x9391054a,
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0x91054a7f, 0x054a7f09, 0x4a7f09d5, 0x7f09d5f4,
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0x09d5f47d, 0xd5f47d4d, 0xf47d4d37, 0x7d4d3770,
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0x4d377036, 0x377036d8, 0x7036d8a5, 0x36d8a566,
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0xd8a5664f, 0xa5664f10, 0x664f10e4, 0x4f10e410,
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0x10e41000, 0xe4100000
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};
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uint32_t xi = numext::as_uint(xf);
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// Below, -118 = -126 + 8.
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// -126 is to get the exponent,
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// +8 is to enable alignment of 2/pi's bits on 8 bits.
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// This is possible because the fractional part of x as only 24 meaningful bits.
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uint32_t e = (xi >> 23) - 118;
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// Extract the mantissa and shift it to align it wrt the exponent
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xi = ((xi & 0x007fffffu)| 0x00800000u) << (e & 0x7);
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uint32_t i = e >> 3;
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uint32_t twoopi_1 = two_over_pi[i-1];
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uint32_t twoopi_2 = two_over_pi[i+3];
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uint32_t twoopi_3 = two_over_pi[i+7];
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// Compute x * 2/pi in 2.62-bit fixed-point format.
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uint64_t p;
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p = uint64_t(xi) * twoopi_3;
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p = uint64_t(xi) * twoopi_2 + (p >> 32);
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p = (uint64_t(xi * twoopi_1) << 32) + p;
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// Round to nearest: add 0.5 and extract integral part.
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uint64_t q = (p + zero_dot_five) >> 62;
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*quadrant = int(q);
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// Now it remains to compute "r = x - q*pi/2" with high accuracy,
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// since we have p=x/(pi/2) with high accuracy, we can more efficiently compute r as:
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// r = (p-q)*pi/2,
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// where the product can be be carried out with sufficient accuracy using double precision.
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p -= q<<62;
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return float(double(int64_t(p)) * pio2_62);
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}
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template<bool ComputeSine,typename Packet>
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EIGEN_DEFINE_FUNCTION_ALLOWING_MULTIPLE_DEFINITIONS
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@ -285,17 +338,6 @@ Packet psincos_float(const Packet& _x)
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PacketI y_int = preinterpret<PacketI>(y_round); // last 23 digits represent integer (if abs(x)<2^24)
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y = psub(y_round, cst_rounding_magic); // nearest integer to x*4/pi
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// Compute the sign to apply to the polynomial.
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// sin: sign = second_bit(y_int) xor signbit(_x)
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// cos: sign = second_bit(y_int+1)
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Packet sign_bit = ComputeSine ? pxor(_x, preinterpret<Packet>(pshiftleft<30>(y_int)))
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: preinterpret<Packet>(pshiftleft<30>(padd(y_int,csti_1)));
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sign_bit = pand(sign_bit, cst_sign_mask); // clear all but left most bit
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// Get the polynomial selection mask from the second bit of y_int
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// We'll calculate both (sin and cos) polynomials and then select from the two.
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Packet poly_mask = preinterpret<Packet>(pcmp_eq(pand(y_int, csti_1), pzero(y_int)));
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// Reduce x by y octants to get: -Pi/4 <= x <= +Pi/4
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// using "Extended precision modular arithmetic"
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#if defined(EIGEN_HAS_SINGLE_INSTRUCTION_MADD)
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@ -332,28 +374,35 @@ Packet psincos_float(const Packet& _x)
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// The coefficients are: 0xbfc90f80, 0xb7354480, 0x2e74b9ee
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#endif
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// We use huge_vals as a temporary for abs(_x) to ensure huge_vals
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// is fully initialized for the last pselect(). (prevent compiler warning)
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Packet huge_vals = pabs(_x);
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Packet huge_mask = pcmp_le(pset1<Packet>(huge_th),huge_vals);
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if(predux_any(huge_mask))
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if(predux_any(pcmp_le(pset1<Packet>(huge_th),pabs(_x))))
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{
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const int PacketSize = unpacket_traits<Packet>::size;
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#if EIGEN_HAS_CXX11
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alignas(Packet) float vals[PacketSize];
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#else
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EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float vals[PacketSize];
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#endif
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pstoreu(vals, _x);
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for(int k=0; k<PacketSize;++k) {
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EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) float x_cpy[PacketSize];
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EIGEN_ALIGN_TO_BOUNDARY(sizeof(Packet)) int y_int2[PacketSize];
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pstoreu(vals, pabs(_x));
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pstoreu(x_cpy, x);
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pstoreu(y_int2, y_int);
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for(int k=0; k<PacketSize;++k)
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{
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float val = vals[k];
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if(numext::abs(val)>=huge_th) {
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vals[k] = ComputeSine ? std::sin(val) : std::cos(val);
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if(val>=huge_th && (numext::isfinite)(val))
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x_cpy[k] = trig_reduce_huge(val,&y_int2[k]);
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}
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x = ploadu<Packet>(x_cpy);
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y_int = ploadu<PacketI>(y_int2);
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}
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huge_vals = ploadu<Packet>(vals);
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}
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// Compute the sign to apply to the polynomial.
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// sin: sign = second_bit(y_int) xor signbit(_x)
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// cos: sign = second_bit(y_int+1)
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Packet sign_bit = ComputeSine ? pxor(_x, preinterpret<Packet>(pshiftleft<30>(y_int)))
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: preinterpret<Packet>(pshiftleft<30>(padd(y_int,csti_1)));
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sign_bit = pand(sign_bit, cst_sign_mask); // clear all but left most bit
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// Get the polynomial selection mask from the second bit of y_int
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// We'll calculate both (sin and cos) polynomials and then select from the two.
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Packet poly_mask = preinterpret<Packet>(pcmp_eq(pand(y_int, csti_1), pzero(y_int)));
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Packet x2 = pmul(x,x);
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@ -383,7 +432,7 @@ Packet psincos_float(const Packet& _x)
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: pselect(poly_mask,y1,y2);
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// Update the sign and filter huge inputs
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return pselect(huge_mask, huge_vals, pxor(y, sign_bit));
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return pxor(y, sign_bit);
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}
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template<typename Packet>
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@ -29,10 +29,15 @@
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*
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* If we made alignment depend on whether or not EIGEN_VECTORIZE is defined, it would be impossible to link
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* vectorized and non-vectorized code.
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*
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* FIXME: this code can be cleaned up once we switch to proper C++11 only.
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*/
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#if (defined EIGEN_CUDACC)
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#define EIGEN_ALIGN_TO_BOUNDARY(n) __align__(n)
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#define EIGEN_ALIGNOF(x) __alignof(x)
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#elif EIGEN_HAS_ALIGNAS
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#define EIGEN_ALIGN_TO_BOUNDARY(n) alignas(n)
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#define EIGEN_ALIGNOF(x) alignof(x)
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#elif EIGEN_COMP_GNUC || EIGEN_COMP_PGI || EIGEN_COMP_IBM || EIGEN_COMP_ARM
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#define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n)))
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#define EIGEN_ALIGNOF(x) __alignof(x)
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@ -44,7 +49,7 @@
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#define EIGEN_ALIGN_TO_BOUNDARY(n) __attribute__((aligned(n)))
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#define EIGEN_ALIGNOF(x) __alignof(x)
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#else
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#error Please tell me what is the equivalent of __attribute__((aligned(n))) and __alignof(x) for your compiler
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#error Please tell me what is the equivalent of alignas(n) and alignof(x) for your compiler
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#endif
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// If the user explicitly disable vectorization, then we also disable alignment
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@ -129,16 +129,21 @@
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#define EIGEN_COMP_MSVC_STRICT 0
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#endif
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/// \internal EIGEN_COMP_IBM set to 1 if the compiler is IBM XL C++
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#if defined(__IBMCPP__) || defined(__xlc__)
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#define EIGEN_COMP_IBM 1
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/// \internal EIGEN_COMP_IBM set to xlc version if the compiler is IBM XL C++
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// XLC version
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// 3.1 0x0301
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// 4.5 0x0405
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// 5.0 0x0500
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// 12.1 0x0C01
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#if defined(__IBMCPP__) || defined(__xlc__) || defined(__ibmxl__)
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#define EIGEN_COMP_IBM __xlC__
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#else
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#define EIGEN_COMP_IBM 0
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#endif
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/// \internal EIGEN_COMP_PGI set to 1 if the compiler is Portland Group Compiler
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/// \internal EIGEN_COMP_PGI set to PGI version if the compiler is Portland Group Compiler
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#if defined(__PGI)
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#define EIGEN_COMP_PGI 1
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#define EIGEN_COMP_PGI (__PGIC__*100+__PGIC_MINOR__)
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#else
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#define EIGEN_COMP_PGI 0
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#endif
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@ -347,9 +352,17 @@
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#define EIGEN_OS_WIN_STRICT 0
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#endif
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/// \internal EIGEN_OS_SUN set to 1 if the OS is SUN
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/// \internal EIGEN_OS_SUN set to __SUNPRO_C if the OS is SUN
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// compiler solaris __SUNPRO_C
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// version studio
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// 5.7 10 0x570
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// 5.8 11 0x580
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// 5.9 12 0x590
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// 5.10 12.1 0x5100
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// 5.11 12.2 0x5110
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// 5.12 12.3 0x5120
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#if (defined(sun) || defined(__sun)) && !(defined(__SVR4) || defined(__svr4__))
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#define EIGEN_OS_SUN 1
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#define EIGEN_OS_SUN __SUNPRO_C
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#else
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#define EIGEN_OS_SUN 0
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#endif
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@ -546,6 +559,22 @@
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#endif
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#endif
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#ifndef EIGEN_HAS_ALIGNAS
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#if EIGEN_MAX_CPP_VER>=11 && EIGEN_HAS_CXX11 && \
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( __has_feature(cxx_alignas) \
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|| EIGEN_HAS_CXX14 \
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|| (EIGEN_COMP_MSVC >= 1800) \
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|| (EIGEN_GNUC_AT_LEAST(4,8)) \
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|| (EIGEN_COMP_CLANG>=305) \
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|| (EIGEN_COMP_ICC>=1500) \
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|| (EIGEN_COMP_PGI>=1500) \
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|| (EIGEN_COMP_SUN>=0x5130))
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#define EIGEN_HAS_ALIGNAS 1
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#else
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#define EIGEN_HAS_ALIGNAS 0
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#endif
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#endif
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// Does the compiler support type_traits?
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// - full support of type traits was added only to GCC 5.1.0.
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// - 20150626 corresponds to the last release of 4.x libstdc++
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@ -636,8 +636,41 @@ template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC
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bool not_equal_strict(const double& x,const double& y) { return std::not_equal_to<double>()(x,y); }
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#endif
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/** \internal extract the bits of the float \a x */
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inline unsigned int as_uint(float x)
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{
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unsigned int ret;
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std::memcpy(&ret, &x, sizeof(float));
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return ret;
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}
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} // end namespace numext
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} // end namespace Eigen
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// Define portable (u)int{32,64} types
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#if EIGEN_HAS_CXX11
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#include <cstdint>
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namespace Eigen {
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namespace numext {
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typedef std::uint32_t uint32_t;
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typedef std::int32_t int32_t;
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typedef std::uint64_t uint64_t;
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typedef std::int64_t int64_t;
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}
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}
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#else
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// Without c++11, all compilers able to compile Eigen also
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// provides the C99 stdint.h header file.
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#include <stdint.h>
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namespace Eigen {
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namespace numext {
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typedef ::uint32_t uint32_t;
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typedef ::int32_t int32_t;
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typedef ::uint64_t uint64_t;
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typedef ::int64_t int64_t;
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}
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}
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#endif
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#endif // EIGEN_META_H
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@ -97,6 +97,9 @@ template<int Mode> struct transform_make_affine;
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* - #AffineCompact: the transformation is stored as a (Dim)x(Dim+1) matrix.
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* - #Projective: the transformation is stored as a (Dim+1)^2 matrix
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* without any assumption.
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* - #Isometry: same as #Affine with the additional assumption that
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* the linear part represents a rotation. This assumption is exploited
|
||||
* to speed up some functions such as inverse() and rotation().
|
||||
* \tparam _Options has the same meaning as in class Matrix. It allows to specify DontAlign and/or RowMajor.
|
||||
* These Options are passed directly to the underlying matrix type.
|
||||
*
|
||||
@ -252,11 +255,11 @@ protected:
|
||||
public:
|
||||
|
||||
/** Default constructor without initialization of the meaningful coefficients.
|
||||
* If Mode==Affine, then the last row is set to [0 ... 0 1] */
|
||||
* If Mode==Affine or Mode==Isometry, then the last row is set to [0 ... 0 1] */
|
||||
EIGEN_DEVICE_FUNC inline Transform()
|
||||
{
|
||||
check_template_params();
|
||||
internal::transform_make_affine<(int(Mode)==Affine) ? Affine : AffineCompact>::run(m_matrix);
|
||||
internal::transform_make_affine<(int(Mode)==Affine || int(Mode)==Isometry) ? Affine : AffineCompact>::run(m_matrix);
|
||||
}
|
||||
|
||||
EIGEN_DEVICE_FUNC inline Transform(const Transform& other)
|
||||
|
||||
@ -18,6 +18,7 @@ template<typename _MatrixType> struct traits<FullPivLU<_MatrixType> >
|
||||
{
|
||||
typedef MatrixXpr XprKind;
|
||||
typedef SolverStorage StorageKind;
|
||||
typedef int StorageIndex;
|
||||
enum { Flags = 0 };
|
||||
};
|
||||
|
||||
@ -64,7 +65,6 @@ template<typename _MatrixType> class FullPivLU
|
||||
typedef SolverBase<FullPivLU> Base;
|
||||
|
||||
EIGEN_GENERIC_PUBLIC_INTERFACE(FullPivLU)
|
||||
// FIXME StorageIndex defined in EIGEN_GENERIC_PUBLIC_INTERFACE should be int
|
||||
enum {
|
||||
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
|
||||
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
|
||||
@ -529,8 +529,8 @@ void FullPivLU<MatrixType>::computeInPlace()
|
||||
m_nonzero_pivots = k;
|
||||
for(Index i = k; i < size; ++i)
|
||||
{
|
||||
m_rowsTranspositions.coeffRef(i) = i;
|
||||
m_colsTranspositions.coeffRef(i) = i;
|
||||
m_rowsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
|
||||
m_colsTranspositions.coeffRef(i) = internal::convert_index<StorageIndex>(i);
|
||||
}
|
||||
break;
|
||||
}
|
||||
@ -541,8 +541,8 @@ void FullPivLU<MatrixType>::computeInPlace()
|
||||
// Now that we've found the pivot, we need to apply the row/col swaps to
|
||||
// bring it to the location (k,k).
|
||||
|
||||
m_rowsTranspositions.coeffRef(k) = row_of_biggest_in_corner;
|
||||
m_colsTranspositions.coeffRef(k) = col_of_biggest_in_corner;
|
||||
m_rowsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(row_of_biggest_in_corner);
|
||||
m_colsTranspositions.coeffRef(k) = internal::convert_index<StorageIndex>(col_of_biggest_in_corner);
|
||||
if(k != row_of_biggest_in_corner) {
|
||||
m_lu.row(k).swap(m_lu.row(row_of_biggest_in_corner));
|
||||
++number_of_transpositions;
|
||||
|
||||
@ -19,6 +19,7 @@ template<typename _MatrixType> struct traits<PartialPivLU<_MatrixType> >
|
||||
{
|
||||
typedef MatrixXpr XprKind;
|
||||
typedef SolverStorage StorageKind;
|
||||
typedef int StorageIndex;
|
||||
typedef traits<_MatrixType> BaseTraits;
|
||||
enum {
|
||||
Flags = BaseTraits::Flags & RowMajorBit,
|
||||
@ -80,7 +81,6 @@ template<typename _MatrixType> class PartialPivLU
|
||||
typedef _MatrixType MatrixType;
|
||||
typedef SolverBase<PartialPivLU> Base;
|
||||
EIGEN_GENERIC_PUBLIC_INTERFACE(PartialPivLU)
|
||||
// FIXME StorageIndex defined in EIGEN_GENERIC_PUBLIC_INTERFACE should be int
|
||||
enum {
|
||||
MaxRowsAtCompileTime = MatrixType::MaxRowsAtCompileTime,
|
||||
MaxColsAtCompileTime = MatrixType::MaxColsAtCompileTime
|
||||
|
||||
@ -612,6 +612,62 @@ template<typename Scalar, int Dim, int Options> void transform_products()
|
||||
VERIFY_IS_APPROX((ac*p).matrix(), a_m*p_m);
|
||||
}
|
||||
|
||||
template<typename Scalar, int Mode, int Options> void transformations_no_scale()
|
||||
{
|
||||
/* this test covers the following files:
|
||||
Cross.h Quaternion.h, Transform.h
|
||||
*/
|
||||
typedef Matrix<Scalar,3,1> Vector3;
|
||||
typedef Matrix<Scalar,4,1> Vector4;
|
||||
typedef Quaternion<Scalar> Quaternionx;
|
||||
typedef AngleAxis<Scalar> AngleAxisx;
|
||||
typedef Transform<Scalar,3,Mode,Options> Transform3;
|
||||
typedef Translation<Scalar,3> Translation3;
|
||||
typedef Matrix<Scalar,4,4> Matrix4;
|
||||
|
||||
Vector3 v0 = Vector3::Random(),
|
||||
v1 = Vector3::Random();
|
||||
|
||||
Transform3 t0, t1, t2;
|
||||
|
||||
Scalar a = internal::random<Scalar>(-Scalar(EIGEN_PI), Scalar(EIGEN_PI));
|
||||
|
||||
Quaternionx q1, q2;
|
||||
|
||||
q1 = AngleAxisx(a, v0.normalized());
|
||||
|
||||
t0 = Transform3::Identity();
|
||||
VERIFY_IS_APPROX(t0.matrix(), Transform3::MatrixType::Identity());
|
||||
|
||||
t0.setIdentity();
|
||||
t1.setIdentity();
|
||||
v1 = Vector3::Ones();
|
||||
t0.linear() = q1.toRotationMatrix();
|
||||
t0.pretranslate(v0);
|
||||
t1.linear() = q1.conjugate().toRotationMatrix();
|
||||
t1.translate(-v0);
|
||||
|
||||
VERIFY((t0 * t1).matrix().isIdentity(test_precision<Scalar>()));
|
||||
|
||||
t1.fromPositionOrientationScale(v0, q1, v1);
|
||||
VERIFY_IS_APPROX(t1.matrix(), t0.matrix());
|
||||
VERIFY_IS_APPROX(t1*v1, t0*v1);
|
||||
|
||||
// translation * vector
|
||||
t0.setIdentity();
|
||||
t0.translate(v0);
|
||||
VERIFY_IS_APPROX((t0 * v1).template head<3>(), Translation3(v0) * v1);
|
||||
|
||||
// Conversion to matrix.
|
||||
Transform3 t3;
|
||||
t3.linear() = q1.toRotationMatrix();
|
||||
t3.translation() = v1;
|
||||
Matrix4 m3 = t3.matrix();
|
||||
VERIFY((m3 * m3.inverse()).isIdentity(test_precision<Scalar>()));
|
||||
// Verify implicit last row is initialized.
|
||||
VERIFY_IS_APPROX(Vector4(m3.row(3)), Vector4(0.0, 0.0, 0.0, 1.0));
|
||||
}
|
||||
|
||||
EIGEN_DECLARE_TEST(geo_transformations)
|
||||
{
|
||||
for(int i = 0; i < g_repeat; i++) {
|
||||
@ -641,5 +697,8 @@ EIGEN_DECLARE_TEST(geo_transformations)
|
||||
|
||||
CALL_SUBTEST_8(( transform_associativity<double,2,ColMajor>(Rotation2D<double>(internal::random<double>()*double(EIGEN_PI))) ));
|
||||
CALL_SUBTEST_8(( transform_associativity<double,3,ColMajor>(Quaterniond::UnitRandom()) ));
|
||||
|
||||
CALL_SUBTEST_9(( transformations_no_scale<double,Affine,AutoAlign>() ));
|
||||
CALL_SUBTEST_9(( transformations_no_scale<double,Isometry,AutoAlign>() ));
|
||||
}
|
||||
}
|
||||
|
||||
@ -18,6 +18,8 @@ typename MatrixType::RealScalar matrix_l1_norm(const MatrixType& m) {
|
||||
|
||||
template<typename MatrixType> void lu_non_invertible()
|
||||
{
|
||||
STATIC_CHECK(( internal::is_same<typename FullPivLU<MatrixType>::StorageIndex,int>::value ));
|
||||
|
||||
typedef typename MatrixType::RealScalar RealScalar;
|
||||
/* this test covers the following files:
|
||||
LU.h
|
||||
@ -191,6 +193,8 @@ template<typename MatrixType> void lu_partial_piv()
|
||||
m1.setRandom();
|
||||
PartialPivLU<MatrixType> plu(m1);
|
||||
|
||||
STATIC_CHECK(( internal::is_same<typename PartialPivLU<MatrixType>::StorageIndex,int>::value ));
|
||||
|
||||
VERIFY_IS_APPROX(m1, plu.reconstructedMatrix());
|
||||
|
||||
m3 = MatrixType::Random(size,size);
|
||||
|
||||
@ -568,6 +568,7 @@ template<typename Scalar,typename Packet> void packetmath_real()
|
||||
h.store(data2, internal::plog(h.load(data1)));
|
||||
VERIFY((numext::isinf)(data2[0]));
|
||||
}
|
||||
if(PacketTraits::HasSqrt)
|
||||
{
|
||||
packet_helper<PacketTraits::HasSqrt,Packet> h;
|
||||
data1[0] = Scalar(-1.0f);
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user