cutcutcodec.core.edit.factor.proba¶

Allow to combine probabilistic laws.

Functions

`compute_cumhist`(expr[, n_per_bars])	Calculate the cumulative histogram of a law combination of reduced uniform probability.
`inv_cumhist`(min_, max_, cumhist)	Reverse the histogram of the estimation of the proba repartition function.

Details

cutcutcodec.core.edit.factor.proba.compute_cumhist(expr: Basic | Real | str, n_per_bars: Integral = 3) → Tensor[source]

Calculate the cumulative histogram of a law combination of reduced uniform probability.

Parameters¶

exprstr or sympy.Basic: The combination expression of uniform laws. The free_symbols variables correspond to the draws of these independent reduced uniform distributions.
n_per_barsint: The average number of samples by bars if there is enouth memory.

Returns¶

minfloat: The minimum value of the output of the expression expr.
maxfloat: The maximum value of the output of the expression expr.
cumhisttorch.Tensor: The float 32 cumulative histogram (1d vector) constitued of 256 bins. the min and max of the histograms match with the min and max of the expr.

Notes¶

This function can use some Gio of RAM.

Examples¶

>>> from cutcutcodec.core.edit.factor.proba import compute_cumhist
>>> min_, max_, cum = compute_cumhist("1 + x1 + x2")
>>> round(min_), round(max_)
(1, 3)
>>> cum.round(decimals=3)
tensor([0.0000, 0.0000, 0.0000, 0.0010, 0.0010, 0.0010, 0.0020, 0.0020, 0.0030,
        0.0030, 0.0040, 0.0040, 0.0050, 0.0060, 0.0070, 0.0080, 0.0090, 0.0100,
        0.0110, 0.0120, 0.0140, 0.0150, 0.0160, 0.0180, 0.0190, 0.0210, 0.0220,
        0.0240, 0.0260, 0.0280, 0.0290, 0.0310, 0.0330, 0.0350, 0.0380, 0.0400,
        0.0420, 0.0440, 0.0470, 0.0490, 0.0510, 0.0540, 0.0570, 0.0590, 0.0620,
        0.0650, 0.0680, 0.0700, 0.0730, 0.0760, 0.0800, 0.0830, 0.0860, 0.0890,
        0.0930, 0.0960, 0.0990, 0.1030, 0.1060, 0.1100, 0.1140, 0.1180, 0.1210,
        0.1250, 0.1290, 0.1330, 0.1370, 0.1410, 0.1460, 0.1500, 0.1540, 0.1580,
        0.1630, 0.1670, 0.1720, 0.1770, 0.1810, 0.1860, 0.1910, 0.1960, 0.2010,
        0.2060, 0.2110, 0.2160, 0.2210, 0.2260, 0.2310, 0.2370, 0.2420, 0.2480,
        0.2530, 0.2590, 0.2640, 0.2700, 0.2760, 0.2820, 0.2880, 0.2930, 0.2990,
        0.3060, 0.3120, 0.3180, 0.3240, 0.3300, 0.3370, 0.3430, 0.3500, 0.3560,
        0.3630, 0.3700, 0.3760, 0.3830, 0.3900, 0.3970, 0.4040, 0.4110, 0.4180,
        0.4250, 0.4330, 0.4400, 0.4470, 0.4550, 0.4620, 0.4700, 0.4770, 0.4850,
        0.4930, 0.5000, 0.5070, 0.5150, 0.5230, 0.5300, 0.5380, 0.5450, 0.5530,
        0.5600, 0.5670, 0.5750, 0.5820, 0.5890, 0.5960, 0.6030, 0.6100, 0.6170,
        0.6240, 0.6300, 0.6370, 0.6440, 0.6500, 0.6570, 0.6630, 0.6700, 0.6760,
        0.6820, 0.6880, 0.6940, 0.7010, 0.7070, 0.7120, 0.7180, 0.7240, 0.7300,
        0.7360, 0.7410, 0.7470, 0.7520, 0.7580, 0.7630, 0.7690, 0.7740, 0.7790,
        0.7840, 0.7890, 0.7940, 0.7990, 0.8040, 0.8090, 0.8140, 0.8190, 0.8230,
        0.8280, 0.8330, 0.8370, 0.8420, 0.8460, 0.8500, 0.8540, 0.8590, 0.8630,
        0.8670, 0.8710, 0.8750, 0.8790, 0.8820, 0.8860, 0.8900, 0.8940, 0.8970,
        0.9010, 0.9040, 0.9070, 0.9110, 0.9140, 0.9170, 0.9200, 0.9240, 0.9270,
        0.9300, 0.9320, 0.9350, 0.9380, 0.9410, 0.9430, 0.9460, 0.9490, 0.9510,
        0.9530, 0.9560, 0.9580, 0.9600, 0.9620, 0.9650, 0.9670, 0.9690, 0.9710,
        0.9720, 0.9740, 0.9760, 0.9780, 0.9790, 0.9810, 0.9820, 0.9840, 0.9850,
        0.9860, 0.9880, 0.9890, 0.9900, 0.9910, 0.9920, 0.9930, 0.9940, 0.9950,
        0.9960, 0.9960, 0.9970, 0.9970, 0.9980, 0.9980, 0.9990, 0.9990, 0.9990,
        1.0000, 1.0000, 1.0000, 1.0000])
>>>

cutcutcodec.core.edit.factor.proba.inv_cumhist(min_: Real, max_: Real, cumhist: Tensor) → list[source]

Reverse the histogram of the estimation of the proba repartition function.

Parameters¶

min_numbers.Real: The minimum value of the reversed repartition function.
max_numbers.Real: The minimum value of the reversed repartition function.
cumhisttorch.Tensor: The strictely growing repartition function between 0 and 1. Only y values are represented like a 1d torch vector. The x axis is implicitely a linspace betwen 0 and 1.

Return¶

invreptorch.Tensor: An estimation of the inverse function (1d vector). The dimension is choose to be the same as the input histogram len.

Examples¶

>>> from cutcutcodec.core.edit.factor.proba import compute_cumhist, inv_cumhist
>>> min_, max_, cum = compute_cumhist("1 + x1 + x2")
>>> inv_cumhist(min_, max_, cum)
tensor([0.9985, 1.0814, 1.1181, 1.1462, 1.1699, 1.1908, 1.2096, 1.2270, 1.2432,
        1.2583, 1.2727, 1.2863, 1.2994, 1.3119, 1.3239, 1.3356, 1.3468, 1.3577,
        1.3683, 1.3785, 1.3885, 1.3983, 1.4079, 1.4172, 1.4263, 1.4352, 1.4440,
        1.4526, 1.4610, 1.4693, 1.4775, 1.4855, 1.4933, 1.5011, 1.5087, 1.5163,
        1.5237, 1.5310, 1.5382, 1.5453, 1.5524, 1.5593, 1.5662, 1.5730, 1.5797,
        1.5863, 1.5929, 1.5994, 1.6058, 1.6121, 1.6184, 1.6246, 1.6308, 1.6369,
        1.6429, 1.6489, 1.6549, 1.6608, 1.6666, 1.6724, 1.6781, 1.6838, 1.6894,
        1.6950, 1.7006, 1.7061, 1.7116, 1.7170, 1.7223, 1.7277, 1.7330, 1.7383,
        1.7435, 1.7487, 1.7538, 1.7590, 1.7641, 1.7691, 1.7741, 1.7791, 1.7841,
        1.7890, 1.7939, 1.7988, 1.8036, 1.8084, 1.8132, 1.8180, 1.8227, 1.8274,
        1.8321, 1.8367, 1.8414, 1.8459, 1.8505, 1.8551, 1.8596, 1.8641, 1.8686,
        1.8730, 1.8775, 1.8819, 1.8863, 1.8906, 1.8950, 1.8993, 1.9036, 1.9079,
        1.9122, 1.9164, 1.9206, 1.9248, 1.9290, 1.9332, 1.9374, 1.9415, 1.9456,
        1.9497, 1.9538, 1.9578, 1.9619, 1.9659, 1.9699, 1.9739, 1.9779, 1.9819,
        1.9860, 1.9905, 1.9947, 1.9986, 2.0025, 2.0065, 2.0105, 2.0145, 2.0185,
        2.0225, 2.0266, 2.0306, 2.0347, 2.0388, 2.0429, 2.0470, 2.0512, 2.0554,
        2.0596, 2.0638, 2.0680, 2.0722, 2.0765, 2.0808, 2.0851, 2.0894, 2.0938,
        2.0981, 2.1025, 2.1069, 2.1114, 2.1158, 2.1203, 2.1248, 2.1293, 2.1339,
        2.1384, 2.1430, 2.1477, 2.1523, 2.1570, 2.1617, 2.1664, 2.1712, 2.1760,
        2.1808, 2.1856, 2.1905, 2.1954, 2.2003, 2.2053, 2.2102, 2.2153, 2.2203,
        2.2254, 2.2306, 2.2357, 2.2409, 2.2461, 2.2514, 2.2567, 2.2620, 2.2674,
        2.2728, 2.2783, 2.2838, 2.2894, 2.2950, 2.3006, 2.3063, 2.3120, 2.3178,
        2.3236, 2.3295, 2.3354, 2.3414, 2.3475, 2.3536, 2.3598, 2.3660, 2.3723,
        2.3786, 2.3850, 2.3915, 2.3981, 2.4047, 2.4114, 2.4182, 2.4251, 2.4320,
        2.4390, 2.4462, 2.4534, 2.4607, 2.4681, 2.4757, 2.4833, 2.4911, 2.4989,
        2.5069, 2.5151, 2.5234, 2.5318, 2.5404, 2.5492, 2.5581, 2.5672, 2.5765,
        2.5861, 2.5959, 2.6059, 2.6161, 2.6267, 2.6376, 2.6488, 2.6605, 2.6725,
        2.6850, 2.6981, 2.7117, 2.7261, 2.7412, 2.7574, 2.7748, 2.7936, 2.8145,
        2.8382, 2.8663, 2.9030, 2.9909])
>>>