def compute_anova_rev_restrict_t(topdir: str, m: int):
# Assemble a large experiment table with all data
neighbors = ["5", "10", "15", "20"]
tolerances = ['0.0', '0.2', '0.4', '0.6', '0.8', '1.0']
dfs = []
for n in neighbors:
for tol in tolerances:
casedir = topdir + '/' + 'nn' + '_' + tol + '_' + n
casetable = ac.compute_stored_runs(casedir, m, None)
casetable['TOL'] = [float(tol)] * 5
casetable['NNN'] = [float(n)] * 5
dfs.append(casetable)
dfa = pd.concat(dfs).reset_index(drop=True)
df = dfa[dfa['TOL'] != 1.0]
# Perform a regression with the data
results = ols('REV ~ C(TOL) + C(NNN) + C(TOL):C(NNN)', data=df).fit()
print(results.summary())
print('\n\n\n')
aov_table = sm.stats.anova_lm(results, typ=2)
print(aov_table)
print('\n\n\n')
mct = MultiComparison(df['REV'], df['TOL'])
mct_results = mct.tukeyhsd()
print(mct_results)
mcn = MultiComparison(df['REV'], df['NNN'])
mcn_results = mcn.tukeyhsd()
print(mcn_results)
def compute_manova_cvg(topdir: str, m: int):
# Assemble a large experiment table with all data
neighbors = ["5", "10", "15", "20"]
tolerances = ['0.0', '0.2', '0.4', '0.6', '0.8', '1.0']
dfs = []
for n in neighbors:
for tol in tolerances:
casedir = topdir + '/' + 'nn' + '_' + tol + '_' + n
casetable = ac.compute_stored_runs(casedir, m, None)
casetable['TOL'] = [float(tol)] * 5
casetable['NNN'] = [float(n)] * 5
dfs.append(casetable)
df = pd.concat(dfs).reset_index(drop=True)
# Perform a regression with the data
endog = np.asarray(df[['K', 'N']])
exog = np.asarray(df[['TOL', 'NNN']])
mod = MANOVA.from_formula('K + N ~ TOL + NNN + NNN:TOL', data=df)
print(mod)
result = mod.mv_test()
print(result)
return mod
def compute_anova_rev(topdir: str, m: int):
# Assemble a large experiment table with all data
tolerances = ['0.0', '0.2', '0.4', '0.6', '0.8', '1.0']
dfs = []
for tol in tolerances:
casedir = topdir + '/' + 'all' + '_' + tol
casetable = ac.compute_stored_runs(casedir, m, None)
casetable['TOL'] = [float(tol)] * 5
dfs.append(casetable)
df = pd.concat(dfs).reset_index(drop=True)
# Perform a regression with the data
results = ols('REV ~ C(TOL)', data=df).fit()
print(results.summary())
print('\n\n\n')
aov_table = sm.stats.anova_lm(results, typ=2)
print(aov_table)
print('\n\n\n')
mc = MultiComparison(df['REV'], df['TOL'])
mc_results = mc.tukeyhsd()
print(mc_results)