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Commita6ad2f2

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14 files changed

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14 files changed

+145
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lines changed

‎python_algorithms/algorithms/ml/nnets/deep_learn/layer.py‎

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@@ -18,7 +18,7 @@ def __init__(self) -> None:
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defforward(self,inputs:Tensor)->Tensor:
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"""
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Product the outputs corresponding to these inputs
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Produce the outputs corresponding to these inputs
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"""
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raiseNotImplementedError
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# Autoregressive Inegrated Moving Average
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fromstatsmodels.tsa.arima_modelimportARIMA
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fromrandomimportrandom
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# contrived dataset
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data= [x+random()forxinrange(1,100)]
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# fit model
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model=ARIMA(data,order=(1,1,1))
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model_fit=model.fit(disp=False)
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# make prediction
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yhat=model_fit.predict(len(data),len(data),typ='levels')
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print(yhat)
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# Autoregressive Moving Average
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fromstatsmodels.tsa.arima_modelimportARMA
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fromrandomimportrandom
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# contrived dataset
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data= [random()forxinrange(1,100)]
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# fit model
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model=ARMA(data,order=(2,1))
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model_fit=model.fit(disp=False)
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# make prediction
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yhat=model_fit.predict(len(data),len(data))
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print(yhat)
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# AR example
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fromstatsmodels.tsa.ar_modelimportAR
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fromrandomimportrandom
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# contrived dataset
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data= [x+random()forxinrange(1,100)]
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# fit model
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model=AR(data)
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model_fit=model.fit()
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# make prediction
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yhat=model_fit.predict(len(data),len(data))
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print(yhat)
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# Holt Winters Exponential Smoothing
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fromstatsmodels.tsa.holtwintersimportExponentialSmoothing
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fromrandomimportrandom
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# contrived dataset
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data= [x+random()forxinrange(1,100)]
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# fit model
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model=ExponentialSmoothing(data)
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model_fit=model.fit()
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# make prediction
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yhat=model_fit.predict(len(data),len(data))
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print(yhat)
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# Moving Average Model
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fromstatsmodels.tsa.arima_modelimportARMA
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fromrandomimportrandom
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# contrived dataset
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data= [x+random()forxinrange(1,100)]
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# fit model
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model=ARMA(data,order=(0,1))
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model_fit=model.fit(disp=False)
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# make prediction
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yhat=model_fit.predict(len(data),len(data))
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print(yhat)
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# Seasonal Autoregressive Integrated Moving Average
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fromstatsmodels.tsa.statespace.sarimaximportSARIMAX
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fromrandomimportrandom
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# contrived dataset
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data= [x+random()forxinrange(1,100)]
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# fit model
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model=SARIMAX(data,order=(1,1,1),seasonal_order=(1,1,1,1))
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model_fit=model.fit(disp=False)
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# make prediction
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yhat=model_fit.predict(len(data),len(data))
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print(yhat)
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# Seasonal Autoregressive Integrated Moving Average with Exogenous Regressors
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fromstatsmodels.tsa.statespace.sarimaximportSARIMAX
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fromrandomimportrandom
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# contrived dataset
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data1= [x+random()forxinrange(1,100)]
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data2= [x+random()forxinrange(101,200)]
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# fit model
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model=SARIMAX(data1,exog=data2,order=(1,1,1),seasonal_order=(0,0,0,0))
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model_fit=model.fit(disp=False)
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# make prediction
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exog2= [200+random()]
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yhat=model_fit.predict(len(data1),len(data1),exog=[exog2])
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print(yhat)
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# Simple Exponential Smoothing
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fromstatsmodels.tsa.holtwintersimportSimpleExpSmoothing
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fromrandomimportrandom
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# contrived dataset
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data= [x+random()forxinrange(1,100)]
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# fit model
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model=SimpleExpSmoothing(data)
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model_fit=model.fit()
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# make prediction
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yhat=model_fit.predict(len(data),len(data))
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print(yhat)
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# Vector Autoregression
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fromstatsmodels.tsa.vector_ar.var_modelimportVAR
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fromrandomimportrandom
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# contrived dataset with dependency
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data=list()
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foriinrange(100):
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v1=i+random()
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v2=v1+random()
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row= [v1,v2]
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data.append(row)
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# fit model
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model=VAR(data)
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model_fit=model.fit()
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# make prediction
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yhat=model_fit.forecast(model_fit.y,steps=1)
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print(yhat)

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