diff options
| author | Ta180m | 2020-05-02 15:28:34 -0500 |
|---|---|---|
| committer | Ta180m | 2020-05-02 15:28:34 -0500 |
| commit | 11f475fd15363b1dcc1ffa430bae70196dbc5254 (patch) | |
| tree | 13f9a7e24561ec4e3f6a652d288b114fcb51c5ae /solver2.py | |
| parent | 2485953be4d3b81ae65244b5b31cb5bf2b5f9524 (diff) | |
Fixed formatting
Diffstat (limited to 'solver2.py')
| -rw-r--r-- | solver2.py | 16 |
1 files changed, 8 insertions, 8 deletions
@@ -147,8 +147,8 @@ class Learner(object): print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) with open(f'out/{args.disease}-{args.mode}-data.csv', 'w+') as file: - file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}\n') - file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}\nActual I: {extended_actual[-1] * correction_factor}') + file.write(f'Beta, {beta}\nGamma, {gamma}\nR0, {beta/gamma}\n') + file.write(f'Predicted I, {prediction.y[1][-1] * int(args.popmodel)}\nActual I, {extended_actual[-1] * correction_factor}') elif args.mode == 'SIR': optimal = minimize( loss_sir, @@ -163,8 +163,8 @@ class Learner(object): print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) with open(f'out/{args.disease}-{args.mode}-data.csv', 'w+') as file: - file.write(f'Beta: {beta}\nGamma: {gamma}\nR0: {beta/gamma}\n') - file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}\nActual I: {extended_actual[-1] * correction_factor}') + file.write(f'Beta, {beta}\nGamma, {gamma}\nR0, {beta/gamma}\n') + file.write(f'Predicted I, {prediction.y[1][-1] * int(args.popmodel)}\nActual I, {extended_actual[-1] * correction_factor}') elif args.mode == 'ESIR': optimal = minimize( loss_esir, @@ -179,8 +179,8 @@ class Learner(object): print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) with open(f'out/{args.disease}-{args.mode}-data.csv', 'w+') as file: - file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nR0: {beta/(gamma + mu)}\n') - file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}\nActual I: {extended_actual[-1] * correction_factor}') + file.write(f'Beta, {beta}\nGamma, {gamma}\nMu, {mu}\nR0, {beta/(gamma + mu)}\n') + file.write(f'Predicted I, {prediction.y[1][-1] * int(args.popmodel)}\nActual I, {extended_actual[-1] * correction_factor}') elif args.mode == 'SEIR': # exposed_data = self.load_exposed(self.country) @@ -197,8 +197,8 @@ class Learner(object): print(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}, Actual I: {extended_actual[-1] * correction_factor}') df = compose_df(prediction, extended_actual, correction_factor, new_index) with open(f'out/{args.disease}-{args.mode}-data.csv', 'w+') as file: - file.write(f'Beta: {beta}\nGamma: {gamma}\nMu: {mu}\nSigma: {sigma}\nR0: {(beta * sigma)/((mu + gamma) * (mu + sigma))}\n') - file.write(f'Predicted I: {prediction.y[1][-1] * int(args.popmodel)}\nActual I: {extended_actual[-1] * correction_factor}') + file.write(f'Beta, {beta}\nGamma, {gamma}\nMu, {mu}\nSigma, {sigma}\nR0, {(beta * sigma)/((mu + gamma) * (mu + sigma))}\n') + file.write(f'Predicted I, {prediction.y[1][-1] * int(args.popmodel)}\nActual I, {extended_actual[-1] * correction_factor}') fig, ax = plt.subplots(figsize=(15, 10)) ax.set_title(f'{args.disease} cases over time ({args.mode} Model)') df.plot(ax=ax) |