forked from bartvdbraak/blender
1c44d08a69
This commit adds some new hashing based data structures to blenlib. All of them use open addressing with probing currently. Furthermore, they support small object optimization, but it is not customizable yet. I'll add support for this when necessary. The following main data structures are included: **Set** A collection of values, where every value must exist at most once. This is similar to a Python `set`. **SetVector** A combination of a Set and a Vector. It supports fast search for elements and maintains insertion order when there are no deletes. All elements are stored in a continuous array. So they can be iterated over using a normal `ArrayRef`. **Map** A set of key-value-pairs, where every key must exist at most once. This is similar to a Python `dict`. **StringMap** A special map for the case when the keys are strings. This case is fairly common and allows for some optimizations. Most importantly, many unnecessary allocations can be avoided by storing strings in a single buffer. Furthermore, the interface of this class uses `StringRef` to avoid unnecessary conversions. This commit is a continuation of rB369d5e8ad2bb7.
116 lines
3.8 KiB
C++
116 lines
3.8 KiB
C++
/* Apache License, Version 2.0 */
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#include "testing/testing.h"
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#include "BLI_math.h"
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/* In tests below, when we are using -1.0f as max_diff value, we actually turn the function into a
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* pure-ULP one. */
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/* Put this here, since we cannot use BLI_assert() in inline math files it seems... */
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TEST(math_base, CompareFFRelativeValid)
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{
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EXPECT_TRUE(sizeof(float) == sizeof(int));
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}
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TEST(math_base, CompareFFRelativeNormal)
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{
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float f1 = 1.99999988f; /* *(float *)&(*(int *)&f2 - 1) */
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float f2 = 2.00000000f;
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float f3 = 2.00000048f; /* *(float *)&(*(int *)&f2 + 2) */
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float f4 = 2.10000000f; /* *(float *)&(*(int *)&f2 + 419430) */
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const float max_diff = FLT_EPSILON * 0.1f;
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EXPECT_TRUE(compare_ff_relative(f1, f2, max_diff, 1));
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EXPECT_TRUE(compare_ff_relative(f2, f1, max_diff, 1));
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EXPECT_TRUE(compare_ff_relative(f3, f2, max_diff, 2));
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EXPECT_TRUE(compare_ff_relative(f2, f3, max_diff, 2));
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EXPECT_FALSE(compare_ff_relative(f3, f2, max_diff, 1));
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EXPECT_FALSE(compare_ff_relative(f2, f3, max_diff, 1));
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EXPECT_FALSE(compare_ff_relative(f3, f2, -1.0f, 1));
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EXPECT_FALSE(compare_ff_relative(f2, f3, -1.0f, 1));
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EXPECT_TRUE(compare_ff_relative(f3, f2, -1.0f, 2));
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EXPECT_TRUE(compare_ff_relative(f2, f3, -1.0f, 2));
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EXPECT_FALSE(compare_ff_relative(f4, f2, max_diff, 64));
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EXPECT_FALSE(compare_ff_relative(f2, f4, max_diff, 64));
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EXPECT_TRUE(compare_ff_relative(f1, f3, max_diff, 64));
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EXPECT_TRUE(compare_ff_relative(f3, f1, max_diff, 64));
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}
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TEST(math_base, CompareFFRelativeZero)
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{
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float f0 = 0.0f;
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float f1 = 4.2038954e-045f; /* *(float *)&(*(int *)&f0 + 3) */
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float fn0 = -0.0f;
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float fn1 = -2.8025969e-045f; /* *(float *)&(*(int *)&fn0 - 2) */
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const float max_diff = FLT_EPSILON * 0.1f;
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EXPECT_TRUE(compare_ff_relative(f0, f1, -1.0f, 3));
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EXPECT_TRUE(compare_ff_relative(f1, f0, -1.0f, 3));
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EXPECT_FALSE(compare_ff_relative(f0, f1, -1.0f, 1));
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EXPECT_FALSE(compare_ff_relative(f1, f0, -1.0f, 1));
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EXPECT_TRUE(compare_ff_relative(fn0, fn1, -1.0f, 8));
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EXPECT_TRUE(compare_ff_relative(fn1, fn0, -1.0f, 8));
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EXPECT_TRUE(compare_ff_relative(f0, f1, max_diff, 1));
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EXPECT_TRUE(compare_ff_relative(f1, f0, max_diff, 1));
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EXPECT_TRUE(compare_ff_relative(fn0, f0, max_diff, 1));
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EXPECT_TRUE(compare_ff_relative(f0, fn0, max_diff, 1));
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EXPECT_TRUE(compare_ff_relative(f0, fn1, max_diff, 1));
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EXPECT_TRUE(compare_ff_relative(fn1, f0, max_diff, 1));
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/* Note: in theory, this should return false, since 0.0f and -0.0f have 0x80000000 diff,
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* but overflow in subtraction seems to break something here
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* (abs(*(int *)&fn0 - *(int *)&f0) == 0x80000000 == fn0), probably because int32 cannot
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* hold this abs value. this is yet another illustration of why one shall never use (near-)zero
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* floats in pure-ULP comparison. */
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// EXPECT_FALSE(compare_ff_relative(fn0, f0, -1.0f, 1024));
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// EXPECT_FALSE(compare_ff_relative(f0, fn0, -1.0f, 1024));
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EXPECT_FALSE(compare_ff_relative(fn0, f1, -1.0f, 1024));
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EXPECT_FALSE(compare_ff_relative(f1, fn0, -1.0f, 1024));
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}
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TEST(math_base, Log2FloorU)
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{
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EXPECT_EQ(log2_floor_u(0), 0);
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EXPECT_EQ(log2_floor_u(1), 0);
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EXPECT_EQ(log2_floor_u(2), 1);
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EXPECT_EQ(log2_floor_u(3), 1);
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EXPECT_EQ(log2_floor_u(4), 2);
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EXPECT_EQ(log2_floor_u(5), 2);
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EXPECT_EQ(log2_floor_u(6), 2);
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EXPECT_EQ(log2_floor_u(7), 2);
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EXPECT_EQ(log2_floor_u(8), 3);
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EXPECT_EQ(log2_floor_u(9), 3);
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EXPECT_EQ(log2_floor_u(123456), 16);
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}
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TEST(math_base, Log2CeilU)
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{
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EXPECT_EQ(log2_ceil_u(0), 0);
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EXPECT_EQ(log2_ceil_u(1), 0);
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EXPECT_EQ(log2_ceil_u(2), 1);
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EXPECT_EQ(log2_ceil_u(3), 2);
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EXPECT_EQ(log2_ceil_u(4), 2);
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EXPECT_EQ(log2_ceil_u(5), 3);
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EXPECT_EQ(log2_ceil_u(6), 3);
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EXPECT_EQ(log2_ceil_u(7), 3);
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EXPECT_EQ(log2_ceil_u(8), 3);
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EXPECT_EQ(log2_ceil_u(9), 4);
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EXPECT_EQ(log2_ceil_u(123456), 17);
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}
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