diff --git a/main.py b/main.py
index bb739e8..973b657 100644
--- a/main.py
+++ b/main.py
@@ -1,82 +1,82 @@
 
 import torch
 import numpy as np
 import random
 import argparse
 # own package
 from weightmatrices.utils import utils
 
 
 # read command line args and kwargs
 parser = argparse.ArgumentParser()
 parser.add_argument("--nhidden", nargs="*", type=int, help="number of hidden neurons", default=100)
 parser.add_argument("--methods", nargs="*", help="methods to be applied to create weight matrix", default='all')
 parser.add_argument("--save", type=bool, help="whether to save results or not", default=True)
 args = parser.parse_args()
 
 
 # data import
 print("loading data")
 data_loader = utils.load_data()
 data_matrix = utils.getbigdatamatrix(data_loader)
 n_in_features = data_matrix.shape[1]
 
 
 # weight matrix creation with different methods
 if 'pca' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import pca
     print("creating weight matrix using PCA")
     for n_h in args.nhidden:
         if n_h <= n_in_features: # Number of requested components <= input dimensionality
             W_pca = pca.get_weightmatrices_pca(data_loader, n_h)
             if args.save:
                 utils.saveweightmatrix('pca'+str(n_h), W_pca)
 
 if 'ica' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import ica
     print("creating weight matrix using ICA")
     for n_h in args.nhidden:
         if n_h <= n_in_features: # Number of requested components <= input dimensionality
             W_ica = ica.get_weightmatrices_ica(data_matrix, n_h)
             if args.save:
                 utils.saveweightmatrix('ica'+str(n_h), W_ica)
 
 if 'sc' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import sc
     print("creating weight matrix using SC")
     for n_h in args.nhidden:
-        W_sc = sc.get_weightmatrices_sc(data_matrix, n_h, getsparsity=True)
+        W_sc = sc.get_weightmatrices_sc(data_matrix, n_h, getsparsity=False)
         if args.save:
             utils.saveweightmatrix('sc'+str(n_h), W_sc)
 
 if 'rg' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import rg
     print("creating weight matrix using RG")
     for n_h in args.nhidden:
         W_rg = rg.get_weightmatrices_rg(data_matrix, n_h)
         if args.save:
             utils.saveweightmatrix('rg'+str(n_h), W_rg)
 
 if 'rp' in args.methods or 'all' in args.methods:
     from weightmatrices.algos import rp
     print("creating weight matrix using RP")
     for n_h in args.nhidden:
         W_rp = rp.get_weightmatrices_rp(data_matrix, n_h)
         if args.save:
             utils.saveweightmatrix('rp'+str(n_h), W_rp)
 
 
 # jump to interactive mode
 import code
 code.interact(local=locals())
 
 
 # sample plotting
 #import matplotlib
 #matplotlib.use('TkAgg')
 #import matplotlib.pyplot as plt
 
 #W = W_pca
 #plt.ion()
 #plt.imshow(W[random.sample(range(0, args.nhidden[-1]), 1)[0], :].reshape(28, 28), cmap = 'gray')
 #plt.show()
diff --git a/weightmatrices/algos/ica.py b/weightmatrices/algos/ica.py
index b11d239..1f18e59 100644
--- a/weightmatrices/algos/ica.py
+++ b/weightmatrices/algos/ica.py
@@ -1,17 +1,17 @@
 # Independent Component Analysis
 
 from sklearn.decomposition import FastICA
 from weightmatrices.utils import utils
 
 def get_ica_trafo_matrix(data_matrix, n_h):
     transformer = FastICA(n_components=n_h, tol=0.001)
     data_transformed = transformer.fit_transform(data_matrix)
     return transformer.components_
 
 def get_weightmatrices_ica(data_matrix, n_h):
-    print("Creating weigth matrix for ICA for "+str(n_h)+" hidden neurons...")
+    print("creating weigth matrix for ICA for "+str(n_h)+" hidden neurons...")
     n_in_features = data_matrix.shape[1]
     assert n_h <= n_in_features, "Number of requested independent components higher than input dimensionality!"
     W = get_ica_trafo_matrix(data_matrix, n_h)
     W = utils.normalise_weightmatrix(W)
     return W
diff --git a/weightmatrices/algos/rg.py b/weightmatrices/algos/rg.py
index 9619e41..7d54e4c 100644
--- a/weightmatrices/algos/rg.py
+++ b/weightmatrices/algos/rg.py
@@ -1,30 +1,31 @@
 # Random Gabors
 
 import numpy as np
 import random
 from weightmatrices.utils import utils
 
 def get_gabor_kernel(lbda, theta, psi, sigma, gamma, N):
     w = np.zeros((N, N))
     rm = np.array([[np.cos(theta), np.sin(theta)],[-np.sin(theta), np.cos(theta)]])
     for x in range(N):
         for y in range(N):
             r = np.matmul(rm, np.array([x, y])-(N/2))
             w[x, y] = np.exp(-(r[0]**2 + gamma**2*r[1]**2)/(2*sigma**2)) * np.cos(2*np.pi*r[0]/lbda + psi)
 
     return w
 
 # these are heuristic boundaries for random Gabors
 def get_random_gabor(N):
     return get_gabor_kernel((2*N-N/4)*random.random()+N/4,
                             2*np.pi*random.random(), 2*np.pi*random.random(),
                             (N-N/8)*random.random()+N/8, random.random(), N)
 
 
 def get_weightmatrices_rg(data_matrix, n_h):
+    print("creating weigth matrix for RG for "+str(n_h)+" hidden neurons...")
     N = int(np.sqrt(data_matrix.shape[1]))
     W = np.zeros((n_h, N**2))
     for i in range(n_h):
         W[i, :] = get_random_gabor(N).flatten()
     W = utils.normalise_weightmatrix(W)
     return W
diff --git a/weightmatrices/algos/rp.py b/weightmatrices/algos/rp.py
index df4a3f9..075e7c7 100644
--- a/weightmatrices/algos/rp.py
+++ b/weightmatrices/algos/rp.py
@@ -1,10 +1,11 @@
 # Random Projections
 
 import numpy as np
 from weightmatrices.utils import utils
 
 def get_weightmatrices_rp(data_matrix, n_h):
+    print("creating weigth matrix for RP for "+str(n_h)+" hidden neurons...")
     N = data_matrix.shape[1]
     W = np.random.randn(n_h, N)
     W = utils.normalise_weightmatrix(W)
     return W