diff --git a/mixfit.py b/mixfit.py
index 569b251..38fdf35 100644
--- a/mixfit.py
+++ b/mixfit.py
@@ -2,11 +2,71 @@
 
 
 def max_likelihood(x, tau, mu1, sigma1, mu2, sigma2, rtol=1e-3):
-    pass
+    sigma1 = 0.5
+    sigma2 = 0.6
+    tau = 0.25
+    mu1 = 0.5
+    sigma1 = 0.2
+    mu2 = 0.9
+    sigma2 = 0.7
+    n = 10000
+    x1 = np.random.normal(mu1,sigma1,size=(int(n*tau)))
+    x2 = np.random.normal(mu2,sigma2,size=(int(n*(1-tau))))
+    x = np.concatenate([x1,x2])
+    
+    def sumlog(T_norm):
+        return -np.sum(np.log(T_norm))
+
+    sigma12 = sigma1**2
+    sigma22 = sigma2**2
+    T1 = (tau/np.sqrt(2*np.pi*sigma12)*np.exp(-0.5*(x-mu1)**2/sigma12))
+    T2 = ((1-tau)/np.sqrt(2*np.pi*sigma22)*np.exp(-0.5*(x-mu2)**2/sigma22))
+    T_norm = T1 + T2
+    
+    T_nov = -np.sum(np.log(T_norm))
+    return T_nov 
+
 
+x = np.array([5,3,8,7])
+print(sp.optimize.minimize(lambda par: max_likelihood(x, *par), x0 = np.array([tau, mu1, mu2, sigma1, sigma2]),
+                           bounds = [(0,0.99), (-400, 400), (-400, 400), (-400, 400), (-400, 400)]).x)   
+    
+    
+  
 
+
+    
 def em_double_gauss(x, tau, mu1, sigma1, mu2, sigma2, rtol=1e-3):
-    pass
+    
+    
+    def normrasp(x, mu, sigma):
+        return 1/(np.sqrt(2*np.pi)*sigma)*np.exp(-(x-mu)**2 / (2*sigma**2))
+    
+    
+    def func(x, tau, mu1, sigma1, mu2, sigma2):
+        return tau * normrasp(x, mu1, sigma1) + (1 - tau)*normrasp(x, mu2, sigma2)
+
+    
+    y = np.array([tau, mu1, sigma1, mu2, sigma2])
+    while True:
+        tau = np.sum(tau * normrasp(x, mu1, sigma1))/ np.sum(func(x, tau, mu1, sigma1, mu2, sigma2))
+        mu1 = np.sum(x * normrasp(x, mu1, sigma1))/np.sum(normrasp(x, mu1, sigma1))
+        mu2 = np.sum(x * normrasp(x, mu2, sigma2))/np.sum(normrasp(x, mu2, sigma2))
+        sigma1 = np.sqrt(np.sum((x-sigma1)**2 * normrasp(x, mu1, sigma1))/np.sum(x * normrasp(x, mu1, sigma1)))
+        sigma2 = np.sqrt(np.sum((x-sigma2)**2 * normrasp(x, mu2, sigma2))/np.sum(x * normrasp(x, mu2, sigma2)))
+        ynov = np.array([tau, mu1, sigma1, mu2, sigma2])
+        
+        if np.linalg.norm(y-ynov) <= rtol:
+            break
+        y = ynov
+      
+ return y  
+
+x = np.array([0.99,0.1,6,4,8,4,3,3])
+m_double_gauss(x, 0.4, 0, 0.2, 0, 1)
+
+
+
 
 
 def em_double_cluster(x, tau1, tau2, muv, mu1, mu2, sigma02, sigmax2, sigmav2, rtol=1e-5):