Either you find a way to get the output with the correct type or you use astype, see the docs, in order to change the type of an array

In your case the following example gives you an array of type np.int

c=random.randn(5,5).astype(np.int)

ham_fields = np.array([], dtype=float)

ham_fields = data[data[:, 0] == 0] 
ham_sum = np.delete((ham_fields.sum(0)),0)  

This line assigns a new array object to ham_fields. The first assignment did nothing for you. In Python variables are not declared at the start.

If data has a int dtype, then so does ham_fields. You could change that with a another assignment

ham_fields = ham_fields.astype(float)

ham_sum has the same dtype as ham_fields, from which it's derived.

Assigning a float to an element of a int dtype array will not change the dtype.

    for i in range(ham_len):
        ham_sum[i] = (ham_sum[i] + self.alpha) / (ham_total + (ham_len * self.alpha))

If self.alpha, ham_total are scalar then you should be able to do

ham_sum = (ham_sum + self.alpha)/(ham_toal + (ham_len * self.alpha))

This makes a new array, which will be float, and assigns it to ham_sum variable. It's a new assignment (not modification) so the float dtype is preserved. Or to make things clear, assign it to a new variable name.