GSL::Histogram.new(n)
GSL::Histogram.new(n, [xmin, xmax])
GSL::Histogram.new(n, xmin, xmax)
GSL::Histogram.alloc(n)
GSL::Histogram.equal_bins_p(h1, h2)
GSL::Histogram#set_ranges(v)
GSL::Histogram#set_ranges_uniform(xmin, xmax)
This method sets the ranges of the existing histogram self to cover the range xmin to xmax uniformly. The values of the histogram bins are reset to zero. The bin ranges are shown as below,
bin[0] corresponds to xmin <= x < xmin + d bin[1] corresponds to xmin + d <= x < xmin + 2 d ...... bin[n-1] corresponds to xmin + (n-1)d <= x < xmax
where d is the bin spacing, d = (xmax-xmin)/n.
GSL::Histogram#increment(x)
GSL::Histogram#fill(x)
GSL::Histogram#accumulate(x, weight)
GSL::Histogram#get(i)
GSL::Hiatogram#get_range(i)
GSL::Histogram#range
Vector::View
object as a reference to the pointer
double *range
in the gsl_histogram
struct.GSL::Histogram#bin
Vector::View
object to access the pointer double *bin
in the gsl_histogram
struct.GSL::Histogram#max
GSL::Histogram#min
GSL::Histogram#bins
GSL::Histogram#reset
GSL::Histogram#find(x)
GSL::Histogram#max_val
GSL::Histogram#max_bin
GSL::Histogram#min_val
GSL::Histogram#min_bin
GSL::Histogram#mean
GSL::Histogram#sigma
GSL::Histogram#sum
GSL::Histogram#add
GSL::Histogram#sub
GSL::Histogram#mul
GSL::Histogram#div
GSL::Histogram#scale
GSL::Histogram#scale!
GSL::Histogram#shift
GSL::Histogram#shift!
GSL::Histogram#fwrite(io)
GSL::Histogram#fwrite(filename)
GSL::Histogram#fread(io)
GSL::Histogram#fread(filename)
GSL::Histogram#fprintf(io, range_format = "%e", bin_format = "%e")
GSL::Histogram#fprintf(filename, range_format = "%e", bin_format = "%e")
GSL::Histogram#fscanf(io)
GSL::Histogram#fscanf(filename)
The probability distribution function for a histogram consists of a set of bins which measure the probability of an event falling into a given range of a continuous variable x. A probability distribution function is defined by the following class, which actually stores the cumulative probability distribution function. This is the natural quantity for generating samples via the inverse transform method, because there is a one-to-one mapping between the cumulative probability distribution and the range [0,1]. It can be shown that by taking a uniform random number in this range and finding its corresponding coordinate in the cumulative probability distribution we obtain samples with the desired probability distribution.
GSL::Histogram::Pdf.new(n)
GSL::Histogram::Pdf.alloc(n)
GSL::Histogram::Pdf#init(h)
GSL::Histogram::Pdf#sample(r)
This method uses r, a uniform random number between zero and one, to compute a single random sample from the probability distribution self. The algorithm used to compute the sample s is given by the following formula,
s = range[i] + delta * (range[i+1] - range[i])
where i is the index which satisfies sum[i] <= r < sum[i+1] and delta is (r - sum[i])/(sum[i+1] - sum[i]).
GSL::Histogram::Pdf#n
GSL::Histogram:Pdf#range
Vector::View
object as a reference to the pointer
double *range
in the gsl_histogram_pdf
struct.GSL::Histogram:Pdf#sum
Vector::View
object as a reference to the pointer
double *sum
in the gsl_histogram_pdf
struct.