diff options
| author | Ta180m | 2020-04-29 15:26:41 -0500 |
|---|---|---|
| committer | GitHub | 2020-04-29 15:26:41 -0500 |
| commit | c53ef15799978448a3c5111222c4a8b406f21662 (patch) | |
| tree | eae276717f441f0250f63dd8cf3bed98d052c285 /solver2.py | |
| parent | 431522bb8acee57f39fbbb245f154351a37f4917 (diff) | |
Update solver2.py
Diffstat (limited to 'solver2.py')
| -rw-r--r-- | solver2.py | 10 |
1 files changed, 5 insertions, 5 deletions
@@ -157,7 +157,7 @@ class Learner(object): new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma) print(f'Predicted I: {prediction.y[1][-1] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) - with open(f'out/{args.disease}-data.csv', 'w+') as file: + with open(f'{args.disease}-data.csv', 'w+') as file: file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}') elif args.mode == 'SIR': optimal = minimize( @@ -172,7 +172,7 @@ class Learner(object): new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma) print(f'Predicted I: {prediction.y[1][-1] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) - with open(f'out/{args.disease}-data.csv', 'w+') as file: + with open(f'{args.disease}-data.csv', 'w+') as file: file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}') elif args.mode == 'ESIR': optimal = minimize( @@ -187,7 +187,7 @@ class Learner(object): new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma, mu = mu) print(f'Predicted I: {prediction.y[1][-1] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) - with open(f'out/{args.disease}-data.csv', 'w+') as file: + with open(f'{args.disease}-data.csv', 'w+') as file: file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nR0: {beta/(gamma + mu)}') elif args.mode == 'SEIR': exposed_data = self.load_exposed(self.country) @@ -204,13 +204,13 @@ class Learner(object): new_index, extended_actual, prediction = self.predict(confirmed_data, beta = beta, gamma = gamma, mu = mu) print(f'Predicted I: {prediction.y[1][-1] * 13500}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) - with open(f'out/{args.disease}-data.csv', 'w+') as file: + with open(f'{args.disease}-data.csv', 'w+') as file: file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nSigma: {sigma}\nR0: {(beta * sigma)/((mu + gamma) * (mu + sigma))}') fig, ax = plt.subplots(figsize=(15, 10)) ax.set_title(f'{args.disease} cases over time ({args.mode} Model)') df.plot(ax=ax) fig.savefig(f"{args.out if args.out != None else args.disease}.png") - df.to_csv(f'out/{args.disease}-prediction.csv') + df.to_csv(f'{args.disease}-prediction.csv') def filter_zeroes(arr): out = np.array(arr) |