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ltcptgeneral 2018-11-26 22:26:07 -06:00
parent 94ecd09e0b
commit b51102b260
2 changed files with 18 additions and 6 deletions
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__pycache__/analysis.cpython-37.pyc
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analysis.py
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@ -102,16 +102,26 @@ from sklearn import *
import time import time
import torch import torch
class error(ValueError):
pass
def _init_device (setting, arg): #initiates computation device for ANNs def _init_device (setting, arg): #initiates computation device for ANNs
if setting == "cuda": if setting == "cuda":
temp = setting + ":" + str(arg) temp = setting + ":" + str(arg)
try:
the_device_woman = torch.device(temp if torch.cuda.is_available() else "cpu") the_device_woman = torch.device(temp if torch.cuda.is_available() else "cpu")
return the_device_woman #name that reference return the_device_woman #name that reference
except:
raise error("could not assign cuda or cpu")
elif setting == "cpu": elif setting == "cpu":
try:
the_device_woman = torch.device("cpu") the_device_woman = torch.device("cpu")
return the_device_woman #name that reference return the_device_woman #name that reference
except:
raise error("could not assign cpu")
else: else:
return "error: specified device does not exist (this is a bad error, either a non existent device was selected or the current device does not have a CPU." raise error("specified device does not exist")
class c_entities: class c_entities:
@ -359,6 +369,8 @@ def load_csv(filepath):
file_array = list(csv.reader(csvfile)) file_array = list(csv.reader(csvfile))
return file_array return file_array
def load_csv():
def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, 'column':c_basic_stats, 'row':r_basic_stats], arg for mode 1 or mode 2 for column or row
if method == 'debug': if method == 'debug':
@ -451,7 +463,7 @@ def basic_stats(data, method, arg): # data=array, mode = ['1d':1d_basic_stats, '
return out return out
else: else:
return ["ERROR: method error"] raise error("method error")
def z_score(point, mean, stdev): #returns z score with inputs of point, mean and standard deviation of spread def z_score(point, mean, stdev): #returns z score with inputs of point, mean and standard deviation of spread
score = (point - mean)/stdev score = (point - mean)/stdev