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#: ../../source/reference/tensor/generated/mars.tensor.histogram_bin_edges.rst:2
msgid "mars.tensor.histogram\\_bin\\_edges"
msgstr ""

#: mars.tensor.histogram_bin_edges:1 of
msgid ""
"Function to calculate only the edges of the bins used by the `histogram` "
"function."
msgstr ""

#: mars.tensor.histogram_bin_edges of
msgid "Parameters"
msgstr ""

#: mars.tensor.histogram_bin_edges:4 of
msgid "Input data. The histogram is computed over the flattened tensor."
msgstr ""

#: mars.tensor.histogram_bin_edges:6 of
msgid ""
"If `bins` is an int, it defines the number of equal-width bins in the "
"given range (10, by default). If `bins` is a sequence, it defines the bin"
" edges, including the rightmost edge, allowing for non-uniform bin "
"widths.  If `bins` is a string from the list below, `histogram_bin_edges`"
" will use the method chosen to calculate the optimal bin width and "
"consequently the number of bins (see `Notes` for more detail on the "
"estimators) from the data that falls within the requested range. While "
"the bin width will be optimal for the actual data in the range, the "
"number of bins will be computed to fill the entire range, including the "
"empty portions. For visualisation, using the 'auto' option is suggested. "
"Weighted data is not supported for automated bin size selection.  'auto'"
"     Maximum of the 'sturges' and 'fd' estimators. Provides good     all "
"around performance.  'fd' (Freedman Diaconis Estimator)     Robust "
"(resilient to outliers) estimator that takes into     account data "
"variability and data size.  'doane'     An improved version of Sturges' "
"estimator that works better     with non-normal datasets.  'scott'     "
"Less robust estimator that that takes into account data     variability "
"and data size.  'stone'     Estimator based on leave-one-out cross-"
"validation estimate of     the integrated squared error. Can be regarded "
"as a generalization     of Scott's rule.  'rice'     Estimator does not "
"take variability into account, only data     size. Commonly overestimates"
" number of bins required.  'sturges'     R's default method, only "
"accounts for data size. Only     optimal for gaussian data and "
"underestimates number of bins     for large non-gaussian datasets.  "
"'sqrt'     Square root (of data size) estimator, used by Excel and     "
"other programs for its speed and simplicity."
msgstr ""

#: mars.tensor.histogram_bin_edges:6 of
msgid ""
"If `bins` is an int, it defines the number of equal-width bins in the "
"given range (10, by default). If `bins` is a sequence, it defines the bin"
" edges, including the rightmost edge, allowing for non-uniform bin "
"widths."
msgstr ""

#: mars.tensor.histogram_bin_edges:11 of
msgid ""
"If `bins` is a string from the list below, `histogram_bin_edges` will use"
" the method chosen to calculate the optimal bin width and consequently "
"the number of bins (see `Notes` for more detail on the estimators) from "
"the data that falls within the requested range. While the bin width will "
"be optimal for the actual data in the range, the number of bins will be "
"computed to fill the entire range, including the empty portions. For "
"visualisation, using the 'auto' option is suggested. Weighted data is not"
" supported for automated bin size selection."
msgstr ""

#: mars.tensor.histogram_bin_edges:23 of
msgid "'auto'"
msgstr ""

#: mars.tensor.histogram_bin_edges:22 of
msgid ""
"Maximum of the 'sturges' and 'fd' estimators. Provides good all around "
"performance."
msgstr ""

#: mars.tensor.histogram_bin_edges:27 mars.tensor.histogram_bin_edges:100 of
msgid "'fd' (Freedman Diaconis Estimator)"
msgstr ""

#: mars.tensor.histogram_bin_edges:26 of
msgid ""
"Robust (resilient to outliers) estimator that takes into account data "
"variability and data size."
msgstr ""

#: mars.tensor.histogram_bin_edges:31 mars.tensor.histogram_bin_edges:137 of
msgid "'doane'"
msgstr ""

#: mars.tensor.histogram_bin_edges:30 of
msgid ""
"An improved version of Sturges' estimator that works better with non-"
"normal datasets."
msgstr ""

#: mars.tensor.histogram_bin_edges:35 mars.tensor.histogram_bin_edges:110 of
msgid "'scott'"
msgstr ""

#: mars.tensor.histogram_bin_edges:34 of
msgid ""
"Less robust estimator that that takes into account data variability and "
"data size."
msgstr ""

#: mars.tensor.histogram_bin_edges:40 of
msgid "'stone'"
msgstr ""

#: mars.tensor.histogram_bin_edges:38 of
msgid ""
"Estimator based on leave-one-out cross-validation estimate of the "
"integrated squared error. Can be regarded as a generalization of Scott's "
"rule."
msgstr ""

#: mars.tensor.histogram_bin_edges:44 mars.tensor.histogram_bin_edges:117 of
msgid "'rice'"
msgstr ""

#: mars.tensor.histogram_bin_edges:43 of
msgid ""
"Estimator does not take variability into account, only data size. "
"Commonly overestimates number of bins required."
msgstr ""

#: mars.tensor.histogram_bin_edges:49 mars.tensor.histogram_bin_edges:125 of
msgid "'sturges'"
msgstr ""

#: mars.tensor.histogram_bin_edges:47 of
msgid ""
"R's default method, only accounts for data size. Only optimal for "
"gaussian data and underestimates number of bins for large non-gaussian "
"datasets."
msgstr ""

#: mars.tensor.histogram_bin_edges:52 mars.tensor.histogram_bin_edges:143 of
msgid "'sqrt'"
msgstr ""

#: mars.tensor.histogram_bin_edges:52 of
msgid ""
"Square root (of data size) estimator, used by Excel and other programs "
"for its speed and simplicity."
msgstr ""

#: mars.tensor.histogram_bin_edges:55 of
msgid ""
"The lower and upper range of the bins.  If not provided, range is simply "
"``(a.min(), a.max())``.  Values outside the range are ignored. The first "
"element of the range must be less than or equal to the second. `range` "
"affects the automatic bin computation as well. While bin width is "
"computed to be optimal based on the actual data within `range`, the bin "
"count will fill the entire range including portions containing no data."
msgstr ""

#: mars.tensor.histogram_bin_edges:63 of
msgid ""
"A tensor of weights, of the same shape as `a`.  Each value in `a` only "
"contributes its associated weight towards the bin count (instead of 1). "
"This is currently not used by any of the bin estimators, but may be in "
"the future."
msgstr ""

#: mars.tensor.histogram_bin_edges of
msgid "Returns"
msgstr ""

#: mars.tensor.histogram_bin_edges:69 of
msgid "**bin_edges** -- The edges to pass into `histogram`"
msgstr ""

#: mars.tensor.histogram_bin_edges of
msgid "Return type"
msgstr ""

#: mars.tensor.histogram_bin_edges:72 of
msgid ":func:`histogram`"
msgstr ""

#: mars.tensor.histogram_bin_edges:75 of
msgid "Notes"
msgstr ""

#: mars.tensor.histogram_bin_edges:76 of
msgid ""
"The methods to estimate the optimal number of bins are well founded in "
"literature, and are inspired by the choices R provides for histogram "
"visualisation. Note that having the number of bins proportional to "
":math:`n^{1/3}` is asymptotically optimal, which is why it appears in "
"most estimators. These are simply plug-in methods that give good starting"
" points for number of bins. In the equations below, :math:`h` is the "
"binwidth and :math:`n_h` is the number of bins. All estimators that "
"compute bin counts are recast to bin width using the `ptp` of the data. "
"The final bin count is obtained from ``np.round(np.ceil(range / h))``."
msgstr ""

#: mars.tensor.histogram_bin_edges:92 of
msgid "'auto' (maximum of the 'sturges' and 'fd' estimators)"
msgstr ""

#: mars.tensor.histogram_bin_edges:88 of
msgid ""
"A compromise to get a good value. For small datasets the Sturges value "
"will usually be chosen, while larger datasets will usually default to FD."
"  Avoids the overly conservative behaviour of FD and Sturges for small "
"and large datasets respectively. Switchover point is usually "
":math:`a.size \\approx 1000`."
msgstr ""

#: mars.tensor.histogram_bin_edges:95 of
msgid "h = 2 \\frac{IQR}{n^{1/3}}"
msgstr ""

#: mars.tensor.histogram_bin_edges:97 of
msgid ""
"The binwidth is proportional to the interquartile range (IQR) and "
"inversely proportional to cube root of a.size. Can be too conservative "
"for small datasets, but is quite good for large datasets. The IQR is very"
" robust to outliers."
msgstr ""

#: mars.tensor.histogram_bin_edges:103 of
msgid "h = \\sigma \\sqrt[3]{\\frac{24 * \\sqrt{\\pi}}{n}}"
msgstr ""

#: mars.tensor.histogram_bin_edges:105 of
msgid ""
"The binwidth is proportional to the standard deviation of the data and "
"inversely proportional to cube root of ``x.size``. Can be too "
"conservative for small datasets, but is quite good for large datasets. "
"The standard deviation is not very robust to outliers. Values are very "
"similar to the Freedman-Diaconis estimator in the absence of outliers."
msgstr ""

#: mars.tensor.histogram_bin_edges:113 of
msgid "n_h = 2n^{1/3}"
msgstr ""

#: mars.tensor.histogram_bin_edges:115 of
msgid ""
"The number of bins is only proportional to cube root of ``a.size``. It "
"tends to overestimate the number of bins and it does not take into "
"account data variability."
msgstr ""

#: mars.tensor.histogram_bin_edges:120 of
msgid "n_h = \\log _{2}n+1"
msgstr ""

#: mars.tensor.histogram_bin_edges:122 of
msgid ""
"The number of bins is the base 2 log of ``a.size``.  This estimator "
"assumes normality of data and is too conservative for larger, non-normal "
"datasets. This is the default method in R's ``hist`` method."
msgstr ""

#: mars.tensor.histogram_bin_edges:128 of
msgid ""
"n_h = 1 + \\log_{2}(n) +             \\log_{2}(1 + "
"\\frac{|g_1|}{\\sigma_{g_1}})  g_1 = mean[(\\frac{x - \\mu}{\\sigma})^3]"
"  \\sigma_{g_1} = \\sqrt{\\frac{6(n - 2)}{(n + 1)(n + 3)}}"
msgstr ""

#: mars.tensor.histogram_bin_edges:135 of
msgid ""
"An improved version of Sturges' formula that produces better estimates "
"for non-normal datasets. This estimator attempts to account for the skew "
"of the data."
msgstr ""

#: mars.tensor.histogram_bin_edges:140 of
msgid "n_h = \\sqrt n"
msgstr ""

#: mars.tensor.histogram_bin_edges:142 of
msgid "The simplest and fastest estimator. Only takes into account the data size."
msgstr ""

#: mars.tensor.histogram_bin_edges:146 of
msgid "Examples"
msgstr ""

#: mars.tensor.histogram_bin_edges:154 of
msgid ""
"For consistency with histogram, a tensor of pre-computed bins is passed "
"through unmodified:"
msgstr ""

#: mars.tensor.histogram_bin_edges:160 of
msgid ""
"This function allows one set of bins to be computed, and reused across "
"multiple histograms:"
msgstr ""

#: mars.tensor.histogram_bin_edges:181 of
msgid ""
"Which gives more easily comparable results than using separate bins for "
"each histogram:"
msgstr ""

