diff --git a/python/src/manocca.py b/python/src/manocca.py
index 21c8f0e1a592b3120c14ac0266656561a16f92be..7ee039733fb31d48a3524b1522dae230053422de 100644
--- a/python/src/manocca.py
+++ b/python/src/manocca.py
@@ -110,11 +110,13 @@ class MANOCCA:
self.predictors = predictors
self.cols_predictors = cols_predictors
self.predictors, self.cols_predictors = pt._extract_cols(self.predictors, self.cols_predictors)
+ self.predictors = scale(self.predictors)
self.covariates = covariates
self.cols_covariates = cols_covariates
if covariates is not None :
self.covariates, self.cols_covariates = pt._extract_cols(self.covariates, self.cols_covariates)
+ self.covariates = scale(self.covariates)
# Parameters
if n_comp :
@@ -141,6 +143,9 @@ class MANOCCA:
# Compute prodV_red if not given
if prodV_red is not None:
self.prodV_red = prodV_red
+ if use_resid and isinstance(covariates, np.ndarray):
+ print("adjusting predictor")
+ self.predictors = np.apply_along_axis(lambda x : pt.adjust_covariates(x,covariates), axis = 0, arr = self.predictors)
else : # If not we preprocess the data and compute prodV and prodV_red
self.prodV = self.get_prodV_para_wrap(self.outputs)
@@ -158,7 +163,13 @@ class MANOCCA:
if use_resid and isinstance(covariates, np.ndarray):
print("computing residuals")
+ print("adjusting outputs")
self.prodV_red = np.apply_along_axis(lambda x : pt.adjust_covariates(x,covariates), axis = 0, arr = self.prodV_red)
+ print("adjusting predictor")
+ self.predictors = np.apply_along_axis(lambda x : pt.adjust_covariates(x,covariates), axis = 0, arr = self.predictors)
+ # print("adjusting predictor")
+ # self.predictors = np.apply_along_axis(lambda x : pt.adjust_covariates(x,covariates), axis = 0, arr = self.predictors)
+ # print(self.predictors.mean())
### Functions to generate prodV and prodV_red ###
@@ -287,6 +298,7 @@ class MANOCCA:
if isinstance(var, type(None)) :
var = self.predictors
+
n_preds = var.shape[1]
p = np.empty((n_preds,1))
for i_pred in range(n_preds):
@@ -297,7 +309,7 @@ class MANOCCA:
p[i_pred] = custom_mancova(scale(self.prodV_red[~nan_mask,min_comp:n_comp]), scale(var[~nan_mask,i_pred].reshape(-1,1)), scale(self.covariates[~nan_mask,:]))
else :
# print("Without covariates")
- p[i_pred] = custom_manova(self.prodV_red[~nan_mask,min_comp:n_comp], scale(var[~nan_mask,i_pred].reshape(-1,1)))
+ p[i_pred] = custom_manova(self.prodV_red[~nan_mask,min_comp:n_comp], var[~nan_mask,i_pred].reshape(-1,1))
self.p = p
diff --git a/python/src/manova.py b/python/src/manova.py
index 345a191d7758a7a034de55a2ddcbb8faa0f8f2b8..a8d56eebdf73951b5ac0c05a0d9fa4ad724425d5 100644
--- a/python/src/manova.py
+++ b/python/src/manova.py
@@ -35,6 +35,8 @@ class MANOVA :
if use_resid and isinstance(covariates, np.ndarray):
print("computing residuals")
self.outputs = np.apply_along_axis(lambda x : pt.adjust_covariates(x,covariates), axis = 0, arr = self.outputs)
+ print("adjusting predictor")
+ self.predictors = np.apply_along_axis(lambda x : pt.adjust_covariates(x,covariates), axis = 0, arr = self.predictors)
diff --git a/python/src/simulation.py b/python/src/simulation.py
index 2530f17e4e0fdb8a7c7c208eaf1b8af1570b7afa..6206e9ca50732466f94c0f5c0d71c388960edff1 100644
--- a/python/src/simulation.py
+++ b/python/src/simulation.py
@@ -77,7 +77,6 @@ class Simu :
return self.L_models[num]
def run_simu(self, n_comp, var = None):
-
self.p = None
for m in self.L_models :
m.test(var = var, n_comp = n_comp)
@@ -113,6 +112,10 @@ class Simu :
def _null_loop(self, m, var_null, N_iters, n_comp = None ) :
p_null = np.empty((N_iters, 1))
for i in range(N_iters):
+ # print(var_null)
+ # print(var_null.flags)
+ # var_null.setflags(write=1)
+ # print(var_null.flags)
np.random.shuffle(var_null)
# print(var_null.shape)
m.test(var_null, n_comp = n_comp)
@@ -120,10 +123,10 @@ class Simu :
return p_null
def simu_null(self, var_name, model, Nsimu = 1000, n_comp = None):
- var_pos = self.cols_predictors.index(var_name)
- var_null = self.predictors[:,var_pos].copy()
- var_null = var_null.reshape(-1,1)
m = self.L_models[self.methods.index(model)]
+ var_pos = m.cols_predictors.index(var_name)
+ var_null = m.predictors[:,var_pos].copy()
+ var_null = var_null.reshape(-1,1)
if self.n_jobs == 1 :
return self._null_loop(m, var_null, Nsimu, n_comp)
diff --git a/python/src/tools/compute_manova.py b/python/src/tools/compute_manova.py
index 34d1cbc471af1f1a5e1c08f4eb75dd53734ff781..589199aeac6c8c9a2468580d1c07b171aaa441f9 100644
--- a/python/src/tools/compute_manova.py
+++ b/python/src/tools/compute_manova.py
@@ -18,9 +18,11 @@ def custom_manova(Y,X,C=None, return_beta = False):
beta = linear_regression(Y, X, C)
dot_Y = np.dot(Y.T,Y)
- (sign_num, num) = np.linalg.slogdet(dot_Y-X.shape[0]*np.dot(beta,beta.T))
+ # (sign_num, num) = np.linalg.slogdet(dot_Y-X.shape[0]*np.dot(beta,beta.T))
+ (sign_num, num) = np.linalg.slogdet(dot_Y - np.dot( np.dot(beta, X.T) , np.dot(X, beta.T)))
(sign_denom, denom) = np.linalg.slogdet(dot_Y)
lamb = np.exp(sign_num*num-(sign_denom*denom))
+ # print(lamb)
# lamb = np.exp(num-denom)
# print(sign_num)
@@ -53,6 +55,7 @@ def custom_mancova(Y,X,C=None):
# beta = np.dot(Y.T,X)/X.shape[0]
# print(Y.mean())
# print(X.mean())
+ print("!!! DEPRECIATED !!! ")
Y, beta = linear_regression_test(Y, X, C)
dot_Y = np.dot(Y.T,Y)
@@ -74,7 +77,10 @@ def linear_regression(Y, X, C=None):
beta = np.dot(np.linalg.inv(np.dot(X_C.T,X_C)), np.dot(X_C.T, Y))
beta = beta[-1,:].reshape(-1,1) #-1
else :
- beta = np.dot(Y.T,X)/X.shape[0]
+ # beta = np.dot(Y.T,X)/X.shape[0]
+ beta = np.dot(np.linalg.inv(np.dot(X.T,X)) , np.dot(X.T,Y))
+ beta = beta.T
+ # beta = np.dot(X.T,Y)/X.shape[0]
return beta
diff --git a/python/src/tools/preprocessing_tools.py b/python/src/tools/preprocessing_tools.py
index 9e77695821e3d179f661ad2ae6f5924e016c9cc5..06a539702f9f99f32fd718610106151c477b7f5e 100644
--- a/python/src/tools/preprocessing_tools.py
+++ b/python/src/tools/preprocessing_tools.py
@@ -103,8 +103,10 @@ def adjust_covariates(data_, covariates_):
covariates = covariates_.copy()
nan_mask = ~np.isnan(data) #[True]* data.shape[0]#
- cov = scale(covariates[nan_mask])
+ cov = scale(covariates[nan_mask,:])
dat = scale(data[nan_mask])
+ # cov = covariates[nan_mask,:]
+ # dat = data[nan_mask]
lr = LinearRegression()
lr.fit(cov, dat)
diff --git a/python/src/univariate.py b/python/src/univariate.py
index 84f9904e7b73b62aaedb5f95be379058c1fc106c..ef63592b98967dfad483868e7bbb00ad5e7c4ffb 100644
--- a/python/src/univariate.py
+++ b/python/src/univariate.py
@@ -24,7 +24,7 @@ class UNIVARIATE :
self.outputs, self.cols_outputs = pt._extract_cols(self.outputs, self.cols_outputs)
if len(L_preproc)>0:
self.outputs = pt.pipeline(self.outputs, L_pipe = L_preproc)
- self.outputs = scale(self.outputs)
+ # self.outputs = scale(self.outputs)
self.predictors = predictors
self.cols_predictors = cols_predictors