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This commit is contained in:
ltcptgeneral 2020-01-05 19:06:54 -06:00
parent daecfcc4ee
commit ac420ad9fa
1 changed files with 13 additions and 2 deletions
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data analysis/analysis/analysis.py
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@ -253,7 +253,6 @@ def _init_device(): # initiates computation device for ANNs
device = 'cuda:0' if torch.cuda.is_available() else 'cpu' device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
return device return device
@jit(forceobj=True)
def load_csv(filepath): def load_csv(filepath):
with open(filepath, newline='') as csvfile: with open(filepath, newline='') as csvfile:
file_array = np.array(list(csv.reader(csvfile))) file_array = np.array(list(csv.reader(csvfile)))
@ -270,8 +269,10 @@ 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 = min(data_t)
_max = max(data_t)
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 +433,16 @@ def variance(data):
return np.var(data) return np.var(data)
@jit(nopython=True)
def min(data):
return data.min
@jit(nopython=True)
def max(data):
return data.max
@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"):