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#ifndef TH_GENERIC_FILE
#define TH_GENERIC_FILE "generic/THTensorRandom.c"
#else
void THTensor_(random)(THTensor *self, THGenerator *_generator)
{
#if defined(TH_REAL_IS_BYTE)
TH_TENSOR_APPLY(real, self, *self_data = (unsigned char)(THRandom_random(_generator) % (UCHAR_MAX+1)););
#elif defined(TH_REAL_IS_CHAR)
TH_TENSOR_APPLY(real, self, *self_data = (char)(THRandom_random(_generator) % (CHAR_MAX+1)););
#elif defined(TH_REAL_IS_SHORT)
TH_TENSOR_APPLY(real, self, *self_data = (short)(THRandom_random(_generator) % (SHRT_MAX+1)););
#elif defined(TH_REAL_IS_INT)
TH_TENSOR_APPLY(real, self, *self_data = (int)(THRandom_random(_generator) % (INT_MAX+1UL)););
#elif defined(TH_REAL_IS_LONG)
TH_TENSOR_APPLY(real, self, *self_data = (long)(THRandom_random(_generator) % (LONG_MAX+1UL)););
#elif defined(TH_REAL_IS_FLOAT)
TH_TENSOR_APPLY(real, self, *self_data = (float)(THRandom_random(_generator) % ((1UL << FLT_MANT_DIG)+1)););
#elif defined(TH_REAL_IS_DOUBLE)
TH_TENSOR_APPLY(real, self, *self_data = (double)(THRandom_random(_generator) % ((1ULL << DBL_MANT_DIG)+1)););
#else
#error "Unknown type"
#endif
}
void THTensor_(geometric)(THTensor *self, THGenerator *_generator, double p)
{
TH_TENSOR_APPLY(real, self, *self_data = (real)THRandom_geometric(_generator, p););
}
void THTensor_(bernoulli)(THTensor *self, THGenerator *_generator, double p)
{
TH_TENSOR_APPLY(real, self, *self_data = (real)THRandom_bernoulli(_generator, p););
}
void THTensor_(bernoulli_FloatTensor)(THTensor *self, THGenerator *_generator, THFloatTensor *p)
{
TH_TENSOR_APPLY2(real, self, float, p, *self_data = (real)THRandom_bernoulli(_generator, (double)*p_data););
}
void THTensor_(bernoulli_DoubleTensor)(THTensor *self, THGenerator *_generator, THDoubleTensor *p)
{
TH_TENSOR_APPLY2(real, self, double, p, *self_data = (real)THRandom_bernoulli(_generator, (double)*p_data););
}
#if defined(TH_REAL_IS_FLOAT) || defined(TH_REAL_IS_DOUBLE)
void THTensor_(uniform)(THTensor *self, THGenerator *_generator, double a, double b)
{
TH_TENSOR_APPLY(real, self, *self_data = (real)THRandom_uniform(_generator, a, b););
}
void THTensor_(normal)(THTensor *self, THGenerator *_generator, double mean, double stdv)
{
TH_TENSOR_APPLY(real, self, *self_data = (real)THRandom_normal(_generator, mean, stdv););
}
void THTensor_(exponential)(THTensor *self, THGenerator *_generator, double lambda)
{
TH_TENSOR_APPLY(real, self, *self_data = (real)THRandom_exponential(_generator, lambda););
}
void THTensor_(cauchy)(THTensor *self, THGenerator *_generator, double median, double sigma)
{
TH_TENSOR_APPLY(real, self, *self_data = (real)THRandom_cauchy(_generator, median, sigma););
}
void THTensor_(logNormal)(THTensor *self, THGenerator *_generator, double mean, double stdv)
{
TH_TENSOR_APPLY(real, self, *self_data = (real)THRandom_logNormal(_generator, mean, stdv););
}
void THTensor_(multinomial)(THLongTensor *self, THGenerator *_generator, THTensor *prob_dist, int n_sample, int with_replacement)
{
int start_dim = THTensor_(nDimension)(prob_dist);
long n_dist;
long n_categories;
THDoubleTensor* cum_dist;
int i,j,k;
if (start_dim == 1)
{
THTensor_(resize2d)(prob_dist, 1, THTensor_(size)(prob_dist, 0));
}
n_dist = THTensor_(size)(prob_dist, 0);
n_categories = THTensor_(size)(prob_dist, 1);
THArgCheck(n_sample > 0, 2, "cannot sample n_sample < 0 samples");
if (!with_replacement)
{
THArgCheck((!with_replacement) && (n_sample <= n_categories), 2, \
"cannot sample n_sample > prob_dist:size(1) samples without replacement");
}
/* cumulative probability distribution vector */
cum_dist = THDoubleTensor_newWithSize1d(n_categories);
/* will contain multinomial samples (category indices to be returned) */
THLongTensor_resize2d(self, n_dist , n_sample);
for (i=0; i<n_dist; i++)
{
/* Get normalized cumulative distribution from prob distribution */
double sum = 0;
for (j=0; j<n_categories; j++)
{
sum += THStorage_(get)( \
prob_dist->storage, \
prob_dist->storageOffset+i*prob_dist->stride[0]+j*prob_dist->stride[1] \
);
THDoubleStorage_set(
cum_dist->storage, \
cum_dist->storageOffset+j*cum_dist->stride[0], \
sum \
);
}
THArgCheckWithCleanup((sum > 0), THCleanup(THDoubleTensor_free(cum_dist);), 2,
"invalid multinomial distribution (sum of probabilities <= 0)");
/* normalize cumulative probability distribution so that last val is 1
i.e. doesn't assume original prob_dist row sums to one */
if ( (sum > 0) || ( ( sum < 1.00001) && (sum > 0.99999) ) )
{
for (j=0; j<n_categories; j++)
{
THDoubleTensor_data(cum_dist)[j*cum_dist->stride[0]] /= sum;
}
}
for (j=0; j<n_sample; j++)
{
/* sample a probability mass from a uniform distribution */
double uniform_sample = THRandom_uniform(_generator, 0, 1);
/* Do a binary search for the slot in which the prob falls
ie cum_dist[row][slot-1] < uniform_prob < cum_distr[row][slot] */
int left_pointer = 0;
int right_pointer = n_categories;
int mid_pointer;
double cum_prob;
int sample_idx;
/* Make sure the last cumulative distribution bucket sums to 1 */
THDoubleTensor_data(cum_dist)[(n_categories-1)*cum_dist->stride[0]] = 1;
while(right_pointer - left_pointer > 0)
{
mid_pointer = left_pointer + (right_pointer - left_pointer) / 2;
cum_prob = THDoubleStorage_get( \
cum_dist->storage, \
cum_dist->storageOffset+mid_pointer*cum_dist->stride[0] \
);
if (cum_prob < uniform_sample)
{
left_pointer = mid_pointer + 1;
}
else
{
right_pointer = mid_pointer;
}
}
sample_idx = left_pointer;
/* store in result tensor (will be incremented for lua compat by wrapper) */
THLongStorage_set( \
self->storage, \
self->storageOffset+i*self->stride[0]+j*self->stride[1], \
sample_idx \
);
/* Once a sample is drawn, it cannot be drawn again. ie sample without replacement */
if (!with_replacement)
{
/* update cumulative distribution so that sample cannot be drawn again */
double diff;
double new_val = 0;
double sum;
if (sample_idx != 0)
{
new_val = THDoubleStorage_get( \
cum_dist->storage, \
cum_dist->storageOffset+(sample_idx-1)*cum_dist->stride[0] \
);
}
/* marginal cumulative mass (i.e. original probability) of sample */
diff = THDoubleStorage_get( \
cum_dist->storage, \
cum_dist->storageOffset+sample_idx*cum_dist->stride[0] \
) - new_val;
/* new sum of marginals is not one anymore... */
sum = 1.0 - diff;
for (k=0; k<n_categories; k++)
{
new_val = THDoubleStorage_get( \
cum_dist->storage, \
cum_dist->storageOffset+k*cum_dist->stride[0] \
);
if (k >= sample_idx)
{
/* remove sampled probability mass from later cumulative probabilities */
new_val -= diff;
}
/* make total marginals sum to one */
new_val /= sum;
THDoubleStorage_set( \
cum_dist->storage, \
cum_dist->storageOffset+k*cum_dist->stride[0], \
new_val \
);
}
}
}
}
THDoubleTensor_free(cum_dist);
if (start_dim == 1)
{
THLongTensor_resize1d(self, n_sample);
THTensor_(resize1d)(prob_dist, n_categories);
}
}
#endif
#if defined(TH_REAL_IS_BYTE)
void THTensor_(getRNGState)(THGenerator *_generator, THTensor *self)
{
static const size_t size = sizeof(THGenerator);
THGenerator *rng_state;
THTensor_(resize1d)(self, size);
THArgCheck(THTensor_(nElement)(self) == size, 1, "RNG state is wrong size");
THArgCheck(THTensor_(isContiguous)(self), 1, "RNG state needs to be contiguous");
rng_state = (THGenerator *)THTensor_(data)(self);
THGenerator_copy(rng_state, _generator);
}
void THTensor_(setRNGState)(THGenerator *_generator, THTensor *self)
{
static const size_t size = sizeof(THGenerator);
THGenerator *rng_state;
THArgCheck(THTensor_(nElement)(self) == size, 1, "RNG state is wrong size");
THArgCheck(THTensor_(isContiguous)(self), 1, "RNG state needs to be contiguous");
rng_state = (THGenerator *)THTensor_(data)(self);
THArgCheck(THGenerator_isValid(rng_state), 1, "Invalid RNG state");
THGenerator_copy(_generator, rng_state);
}
#endif
#endif
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