diff --git a/ext/svd.c b/ext/svd.c
index 8145e60..9ce5a45 100644
--- a/ext/svd.c
+++ b/ext/svd.c
@@ -21,7 +21,7 @@ VALUE decompose(VALUE module, VALUE matrix_ruby, VALUE m_ruby, VALUE n_ruby) {
 	/* precondition */
 	if((m*n) != RARRAY_LEN(matrix_ruby)) {
 		rb_raise(rb_eRangeError, "Size of the array is not equal to m * n");
-		return;
+		return 0;
 	}
 	
 	/* convert to u matrix */
diff --git a/lib/svd_matrix.rb b/lib/svd_matrix.rb
index 2e38d22..19bd522 100644
--- a/lib/svd_matrix.rb
+++ b/lib/svd_matrix.rb
@@ -37,16 +37,27 @@ def inspect
   # [ 0, 0, 0, 0 ]
   def decompose(reduce_dimensions_to = nil)
     input_array = []
-    @rows.each {|row| input_array += row}
-    u_array, w_array, v_array = SVD.decompose(input_array, row_size, column_size)
+    # @rows.each {|row| input_array += row}
+    c_count = 0
+    @rows.each do |row| 
+      input_array += row
+    end
+    c_count = input_array.length / row_size
+    puts "input array"
+    puts input_array.length
+    puts "row size"
+    puts row_size
+    puts "column size"
+    puts c_count
+    u_array, w_array, v_array = SVD.decompose(input_array, row_size, c_count)
     
     # recompose U matrix
-    u = SVDMatrix.new(row_size, reduce_dimensions_to || column_size)
-    row_size.times {|i| u.set_row(i, u_array.slice!(0, column_size)[0...(reduce_dimensions_to || column_size)])}
+    u = SVDMatrix.new(row_size, reduce_dimensions_to || c_count)
+    row_size.times {|i| u.set_row(i, u_array.slice!(0, c_count)[0...(reduce_dimensions_to || c_count)])}
     
     # recompose V matrix
-    v = SVDMatrix.new(column_size, reduce_dimensions_to || column_size)
-    column_size.times {|i| v.set_row(i, v_array.slice!(0, column_size)[0...(reduce_dimensions_to || column_size)])}
+    v = SVDMatrix.new(c_count, reduce_dimensions_to || c_count)
+    c_count.times {|i| v.set_row(i, v_array.slice!(0, c_count)[0...(reduce_dimensions_to || c_count)])}
     
     # diagonalise W array as a matrix
     if reduce_dimensions_to
@@ -56,7 +67,7 @@ def decompose(reduce_dimensions_to = nil)
     
     [u, w, v]
   end
-  
+
   # Reduce the number of dimensions of the data to dimensions.
   # Returns a back a recombined matrix (conceptually the original
   # matrix dimensionally reduced). For example Latent Semantic