diff --git a/analysis-master/analysis/analysis.py b/analysis-master/analysis/analysis.py
index c4cf49de..07ff32e6 100644
--- a/analysis-master/analysis/analysis.py
+++ b/analysis-master/analysis/analysis.py
@@ -7,10 +7,12 @@
 #    current benchmark of optimization: 1.33 times faster
 # setup:
 
-__version__ = "1.2.1.002"
+__version__ = "1.2.2.000"
 
 # changelog should be viewed using print(analysis.__changelog__)
 __changelog__ = """changelog:
+	1.2.2.000:
+		- changed output of regressions to function strings instead of list of coefficients
 	1.2.1.002:
 		- renamed ArrayTest class to Array
 	1.2.1.001:
@@ -412,7 +414,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
 			popt, pcov = scipy.optimize.curve_fit(lin, X, y)
 
-			regressions.append((popt.flatten().tolist(), None))
+			coeffs = popt.flatten().tolist()
+			regressions.append(str(coeffs[0]) + "*x+" + str(coeffs[1]))
 
 		except Exception as e:
 
@@ -428,7 +431,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
 			popt, pcov = scipy.optimize.curve_fit(log, X, y)
 
-			regressions.append((popt.flatten().tolist(), None))
+			coeffs = popt.flatten().tolist()
+			regressions.append(str(coeffs[0]) + "*log(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
 
 		except Exception as e:
 			
@@ -444,7 +448,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
 			popt, pcov = scipy.optimize.curve_fit(exp, X, y)
 
-			regressions.append((popt.flatten().tolist(), None))
+			coeffs = popt.flatten().tolist()
+			regressions.append(str(coeffs[0]) + "*e^(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
 
 		except Exception as e:
 
@@ -466,10 +471,14 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
 			params = model.steps[1][1].intercept_.tolist()
 			params = np.append(params, model.steps[1][1].coef_[0].tolist()[1::])
-			params.flatten()
-			params = params.tolist()
-			
-			plys.append(params)
+			params = params.flatten().tolist()
+
+			temp = ""
+			counter = 0
+			for param in params:
+				temp += "(" + str(param) + "*x^" + str(counter) + ")"
+				counter += 1
+			plys.append(temp)
 
 		regressions.append(plys)
 
@@ -483,7 +492,8 @@ def regression(inputs, outputs, args): # inputs, outputs expects N-D array
 
 			popt, pcov = scipy.optimize.curve_fit(sig, X, y)
 
-			regressions.append((popt.flatten().tolist(), None))
+			coeffs = popt.flatten().tolist()
+			regressions.append(str(coeffs[0]) + "*tanh(" + str(coeffs[1]) + "*(x+" + str(coeffs[2]) + "))+" + str(coeffs[3]))
 
 		except Exception as e: