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Added: Created visualizations that depict the traditional approach fo…
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…r model assessment. Changed: Changed RMSE to CRMSE to remove ambiguity.
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AAnzel committed Jun 17, 2023
1 parent 5c8a3af commit 549fa8d
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15,022 changes: 10,012 additions & 5,010 deletions Source/main.ipynb

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18 changes: 10 additions & 8 deletions Source/polar_diagrams/src/polar_diagrams/polar_diagrams.py
Original file line number Diff line number Diff line change
Expand Up @@ -104,14 +104,16 @@ def df_calculate_td_properties(df_input, string_reference_model,
dict_result[string_one_model].append(math.degrees(
math.acos(dict_result[string_one_model][-1])))

# Calculating RMS
for string_one_model in list_all_features:
# Calculating CRMS
dict_result[string_one_model].append(
mean_squared_error(
math.sqrt(mean_squared_error(
df_input[string_reference_model],
df_input[string_one_model], squared=False))
df_input[string_one_model], squared=True) -
(np.mean(df_input[string_reference_model]) -
np.mean(df_input[string_one_model]))**2))

for string_one_model in list_all_features:
# Normalizing the RMS as in the paper
# Normalizing the CRMS as in the paper
dict_result[string_one_model].append(
dict_result[string_one_model][3] /
dict_result[string_reference_model][0])
Expand All @@ -123,8 +125,8 @@ def df_calculate_td_properties(df_input, string_reference_model,

df_result = pd.DataFrame().from_dict(
dict_result, orient='index',
columns=['Standard Deviation', 'Correlation', 'Angle', 'RMS',
'Normalized RMS', 'Normalized Standard Deviation'])
columns=['Standard Deviation', 'Correlation', 'Angle', 'CRMS',
'Normalized CRMS', 'Normalized Standard Deviation'])

df_result = df_result.reset_index().rename(columns={'index': 'Model'})

Expand Down Expand Up @@ -744,7 +746,7 @@ def _chart_create_diagram(list_df_input, string_reference_model,
string_angular_column_label = 'Correlation'
string_tooltip_label_1 = string_radial_column
string_tooltip_label_2 = string_angular_column_label
string_tooltip_label_3 = 'RMS'
string_tooltip_label_3 = 'CRMS'
bool_only_half = True if (
list_df_input[0][string_angular_column] <= 90).all() else False
int_subplot_column_number = 1
Expand Down

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