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analysis.py v 1.1.11.005

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art 2019-12-23 12:48:13 -06:00
parent 0ba3a56ea7
commit ae4ecbd67c
1 changed files with 16 additions and 2 deletions
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data analysis/analysis/analysis.py
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@ -7,10 +7,12 @@
# current benchmark of optimization: 1.33 times faster # current benchmark of optimization: 1.33 times faster
# setup: # setup:
__version__ = "1.1.11.004" __version__ = "1.1.11.005"
# changelog should be viewed using print(analysis.__changelog__) # changelog should be viewed using print(analysis.__changelog__)
__changelog__ = """changelog: __changelog__ = """changelog:
1.1.11.005:
- added min and max for basic_stats
1.1.11.004: 1.1.11.004:
- bug fixes - bug fixes
1.1.11.003: 1.1.11.003:
@ -270,8 +272,10 @@ def basic_stats(data):
_median = median(data_t) _median = median(data_t)
_stdev = stdev(data_t) _stdev = stdev(data_t)
_variance = variance(data_t) _variance = variance(data_t)
_min = npmin(data_t)
_max = npmax()
return _mean, _median, _stdev, _variance return _mean, _median, _stdev, _variance, _min, _max
# returns z score with inputs of point, mean and standard deviation of spread # returns z score with inputs of point, mean and standard deviation of spread
@jit(forceobj=True) @jit(forceobj=True)
@ -432,6 +436,16 @@ def variance(data):
return np.var(data) return np.var(data)
@jit(nopython=True)
def npmin(data):
return np.amin(data)
@jit(nopython=True)
def npmax(data):
return np.amax(data)
@jit(forceobj=True) @jit(forceobj=True)
def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"): def kmeans(data, n_clusters=8, init="k-means++", n_init=10, max_iter=300, tol=0.0001, precompute_distances="auto", verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm="auto"):