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github-python-corpus

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mixed_x = x.detach().clone()
mixed_x[:, bbx1:bbx2, bby1:bby2] = x2[:, bbx1:bbx2, bby1:bby2]
mixed_y = y
if y is not None:
# Adjust lambda
lam = 1.0 - ((bbx2 - bbx1) * (bby2 - bby1) / (x.size()[-1] * x.size()[-2]))
mixed_y = lam * y + (1 - lam) * y2
return mixed_x, mixed_y
class MixingTransforms:
"""Randomly apply only one of MixUp or CutMix. Used for standard training."""
def __init__(self, config_tr: Dict[str, Any], num_classes: int) -> None:
"""Initialize mixup and/or cutmix."""
config_tr = clean_config(config_tr)
self.mixing_transforms = []
if "mixup" in config_tr:
self.mixing_transforms += [MixUp(**config_tr["mixup"])]
if "cutmix" in config_tr:
self.mixing_transforms += [CutMix(**config_tr["cutmix"])]
self.num_classes = num_classes
def __call__(self, images: Tensor, target: Tensor) -> Tuple[Tensor, Tensor]:
"""Apply only one of MixUp or CutMix."""
if len(self.mixing_transforms) > 0:
one_hot_label = F.one_hot(target, num_classes=self.num_classes)
mix_f = random.choice(self.mixing_transforms)
images, target = mix_f(x=images, y=one_hot_label)
return images, target
# <FILESEP>
# -*- coding: utf-8 -*-
"""
Created on Sat Feb 13 13:55:33 2021
@author: saidsa
"""
import numpy as np
import pandas as pd
from MacroData import get_Fama_French_ts
from Utils import Rolling_Regression
######################### Valuation Ratios ###################################
def Price_to_Sales_Signal (stock_object):
return stock_object['PriceClose'] / (stock_object['TotalRevenue'] /stock_object['ShareIssued'])
def Price_to_Earnings_Signal (stock_object):
return stock_object['PriceClose'] / (stock_object['NetIncomeCommonStockholders'] /stock_object['ShareIssued'])
def Price_to_CashFlow_Signal (stock_object):
return stock_object['PriceClose'] / (stock_object['FreeCashFlow'] /stock_object['ShareIssued'])
def Price_to_Book_Signal (stock_object):
return stock_object['PriceClose'] / (stock_object['CommonStockEquity'] /stock_object['ShareIssued'])
def DividendPayout_Ratio_Signal (stock_object):
return stock_object['CashDividendsPaid'] / stock_object['NetIncomeCommonStockholders']
def RetentionRate_Signal (stock_object):
DP = DividendPayout_Ratio_Signal (stock_object)
return 1 - DP
def SustainableGrowth_Signal (stock_object):
RR= RetentionRate_Signal(stock_object)
ROE = ReturnOnEquity_Signal(stock_object)
return RR * ROE
########################## Activity Ratios ####################################
def DaysInventoryOutstanding_Signal(stock_object):
return (stock_object['Inventory'] + 0.5*stock_object['ChangeInInventory']) \
/ ( stock_object['CostOfRevenue'] / 365.0)
def DaysSalesOutstanding_Signal(stock_object):
return (stock_object['AccountsReceivable'] + 0.5*stock_object['ChangesInAccountReceivables']) \
/ ( stock_object['TotalRevenue'] / 365.0)
def DaysPayableOutstanding_Signal(stock_object):
return (stock_object['AccountsPayable'] + 0.5*stock_object['ChangeInAccountPayable']) \
/ ( stock_object['CostOfRevenue'] / 365.0)
def WorkingCapitalTurnover_Signal(stock_object):
return stock_object['TotalRevenue'] / stock_object['WorkingCapital']
def FixedAssetsTurnover_Signal(stock_object):
return stock_object['TotalRevenue'] / (stock_object['TotalAssets'] - stock_object['GoodwillAndOtherIntangibleAssets'])
def TotalAssetsTurnover_Signal(stock_object):
return stock_object['TotalRevenue'] / stock_object['TotalAssets']
######################### Liquidity Ratios ###################################