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99 lines
2.3 KiB
Python
99 lines
2.3 KiB
Python
from functools import reduce
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import inspect
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import numpy as np
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import operator as op
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def sigmoid(x):
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return 1.0 / (1 + np.exp(-x))
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CHOOSE_CACHE = {}
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def choose_using_cache(n, r):
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if n not in CHOOSE_CACHE:
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CHOOSE_CACHE[n] = {}
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if r not in CHOOSE_CACHE[n]:
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CHOOSE_CACHE[n][r] = choose(n, r, use_cache=False)
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return CHOOSE_CACHE[n][r]
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def choose(n, r, use_cache=True):
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if use_cache:
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return choose_using_cache(n, r)
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if n < r:
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return 0
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if r == 0:
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return 1
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denom = reduce(op.mul, range(1, r + 1), 1)
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numer = reduce(op.mul, range(n, n - r, -1), 1)
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return numer // denom
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def get_num_args(function):
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return len(get_parameters(function))
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def get_parameters(function):
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return inspect.signature(function).parameters
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# Just to have a less heavyweight name for this extremely common operation
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#
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# We may wish to have more fine-grained control over division by zero behavior
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# in the future (separate specifiable values for 0/0 and x/0 with x != 0),
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# but for now, we just allow the option to handle indeterminate 0/0.
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def clip(a, min_a, max_a):
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if a < min_a:
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return min_a
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elif a > max_a:
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return max_a
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return a
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def clip_in_place(array, min_val=None, max_val=None):
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if max_val is not None:
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array[array > max_val] = max_val
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if min_val is not None:
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array[array < min_val] = min_val
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return array
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def fdiv(a, b, zero_over_zero_value=None):
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if zero_over_zero_value is not None:
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out = np.full_like(a, zero_over_zero_value)
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where = np.logical_or(a != 0, b != 0)
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else:
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out = None
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where = True
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return np.true_divide(a, b, out=out, where=where)
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def binary_search(function,
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target,
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lower_bound,
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upper_bound,
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tolerance=1e-4):
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lh = lower_bound
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rh = upper_bound
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while abs(rh - lh) > tolerance:
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mh = np.mean([lh, rh])
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lx, mx, rx = [function(h) for h in (lh, mh, rh)]
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if lx == target:
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return lx
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if rx == target:
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return rx
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if lx <= target and rx >= target:
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if mx > target:
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rh = mh
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else:
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lh = mh
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elif lx > target and rx < target:
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lh, rh = rh, lh
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else:
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return None
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return mh
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