forked from personal/squiggle.c
reformat squiggle.c, remake examples.
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@ -4,7 +4,7 @@
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#include <stdint.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <sys/types.h>
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// #include <sys/types.h>
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#include <time.h>
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#define EXIT_ON_ERROR 0
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23
squiggle.c
23
squiggle.c
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@ -3,12 +3,11 @@
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#include <stdlib.h>
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// PI constant
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const float PI = M_PI;// 3.14159265358979323846;
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const float PI = M_PI; // 3.14159265358979323846;
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// Pseudo Random number generator
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uint32_t xorshift32
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(uint32_t* seed)
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uint32_t xorshift32(uint32_t* seed)
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{
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// Algorithm "xor" from p. 4 of Marsaglia, "Xorshift RNGs"
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// See <https://stackoverflow.com/questions/53886131/how-does-xorshift32-works>
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@ -24,8 +23,9 @@ uint32_t xorshift32
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// Distribution & sampling functions
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float rand_0_to_1(uint32_t* seed){
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return ((float) xorshift32(seed)) / ((float) UINT32_MAX);
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float rand_0_to_1(uint32_t* seed)
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{
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return ((float)xorshift32(seed)) / ((float)UINT32_MAX);
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}
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float rand_float(float max, uint32_t* seed)
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@ -33,7 +33,7 @@ float rand_float(float max, uint32_t* seed)
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return rand_0_to_1(seed) * max;
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}
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float ur_normal(uint32_t* seed)
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float unit_normal(uint32_t* seed)
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{
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float u1 = rand_0_to_1(seed);
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float u2 = rand_0_to_1(seed);
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@ -48,7 +48,7 @@ float random_uniform(float from, float to, uint32_t* seed)
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float random_normal(float mean, float sigma, uint32_t* seed)
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{
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return (mean + sigma * ur_normal(seed));
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return (mean + sigma * unit_normal(seed));
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}
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float random_lognormal(float logmean, float logsigma, uint32_t* seed)
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@ -90,10 +90,10 @@ float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint3
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// You can see a simpler version of this function in the git history
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// or in C-02-better-algorithm-one-thread/
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float sum_weights = array_sum(weights, n_dists);
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float* cumsummed_normalized_weights = (float*) malloc(n_dists * sizeof(float));
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cumsummed_normalized_weights[0] = weights[0]/sum_weights;
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float* cumsummed_normalized_weights = (float*)malloc(n_dists * sizeof(float));
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cumsummed_normalized_weights[0] = weights[0] / sum_weights;
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for (int i = 1; i < n_dists; i++) {
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cumsummed_normalized_weights[i] = cumsummed_normalized_weights[i - 1] + weights[i]/sum_weights;
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cumsummed_normalized_weights[i] = cumsummed_normalized_weights[i - 1] + weights[i] / sum_weights;
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}
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float result;
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@ -106,7 +106,8 @@ float mixture(float (*samplers[])(uint32_t*), float* weights, int n_dists, uint3
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break;
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}
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}
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if(result_set_flag == 0) result = samplers[n_dists-1](seed);
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if (result_set_flag == 0)
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result = samplers[n_dists - 1](seed);
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free(cumsummed_normalized_weights);
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return result;
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