## Issue

I have the following two functions:

```
def predict(self, X):
y_pred = [self._predict(x) for x in X]
return np.array(y_pred)
def _predict(self, x):
probs = []
for idx, c in enumerate(self.classes):
prior = self.classesPrior[idx]
probs_c = np.sum(np.log(self.density_function(x, idx, self.classesMean[idx], self.classesVariance[idx])))
probs.append(probs_c + np.log(prior))
return self.classes[np.argmax(probs)]
```

I am trying to compose this code into a function named `predict`

that has the same behavior as observed when using the above two functions.

This is what I tried to do:

```
def predict(self, X):
probs = []
# calculate posterior probability for each class
for idx, c in enumerate(self.classes):
prior = self.classesPrior[idx]
for x in X:
probs_c = np.sum(np.log(self.density_function(x, idx, self.classesMean[idx], self.classesVariance[idx])))
probs.append(np.argmax(probs_c + np.log(prior)))
y_pred = self.classes[probs]
# return class with highest posterior probability
return np.array(y_pred)
```

## Solution

You can remove the list comprehension and use a `for`

loop instead, placing the function definition of `_predict()`

inside the `for`

loop with minor modifications:

```
def predict(self, X):
y_pred = []
for x in X:
probs = []
for idx, c in enumerate(self.classes):
prior = self.classesPrior[idx]
probs_c = np.sum(np.log(self.density_function(x, idx, self.classesMean[idx], self.classesVariance[idx])))
probs.append(probs_c + np.log(prior))
y_pred.append(self.classes[np.argmax(probs)])
return np.array(y_pred)
```

Answered By – BrokenBenchmark

Answer Checked By – Mary Flores (BugsFixing Volunteer)