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@ -253,7 +253,6 @@ def _init_device(): # initiates computation device for ANNs
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device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
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return device
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@jit(forceobj=True)
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def load_csv(filepath):
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with open(filepath, newline='') as csvfile:
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file_array = np.array(list(csv.reader(csvfile)))
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@ -270,8 +269,10 @@ def basic_stats(data):
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_median = median(data_t)
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_stdev = stdev(data_t)
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_variance = variance(data_t)
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_min = min(data_t)
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_max = max(data_t)
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return _mean, _median, _stdev, _variance
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return _mean, _median, _stdev, _variance, _min, _max
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# returns z score with inputs of point, mean and standard deviation of spread
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@jit(forceobj=True)
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@ -432,6 +433,16 @@ def variance(data):
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return np.var(data)
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@jit(nopython=True)
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def min(data):
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return data.min
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@jit(nopython=True)
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def max(data):
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return data.max
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@jit(forceobj=True)
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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"):
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