# Common Workarounds

As explained in the [Basics of FHE](https://docs.zama.ai/concrete/2.5/getting-started/fhe_basics), the challenge for developers is to adapt their code to fit FHE constraints. In this document we have collected some common examples to illustrate the kind of optimization one can do to get better performance.

{% hint style="info" %}
All code snippets provided here are temporary workarounds. In future versions of Concrete, some functions described here could be directly available in a more generic and efficient form. These code snippets are coming from support answers in our [community forum](https://community.zama.ai)
{% endhint %}

## Minimum for Two values

In this first example, we compute a minimum by creating the difference between two numbers `y` and `x` and conditionally remove this diff from `y` to either get `x` if `y>x` or `y` if `x>y`:

```python
import numpy as np
from concrete import fhe

@fhe.compiler({"x": "encrypted", "y": "encrypted"})
def min_two(x, y):
	diff = y - x
	min_x_y = y - np.maximum(y - x, 0)
	return min_x_y

inputset = [tuple(np.random.randint(0, 16, size=2)) for _ in range(50)]
circuit = min_two.compile(inputset)

x, y = np.random.randint(0, 16, size=2)
assert circuit.encrypt_run_decrypt(x, y) == min(x, y)
```

## Maximum for Two values

The companion example of above with the maximum value of two integers instead of the minimum:

```python
import numpy as np
from concrete import fhe

@fhe.compiler({"x": "encrypted", "y": "encrypted"})
def max_two(x, y):
	diff = y - x
	max_x_y = y - np.minimum(y - x, 0)
	return max_x_y

inputset = [tuple(np.random.randint(0, 16, size=2)) for _ in range(50)]
circuit = max_two.compile(inputset)

x, y = np.random.randint(0, 16, size=2)
assert circuit.encrypt_run_decrypt(x, y) == max(x, y)
```

## Minimum for several values

And an extension for more than two values:

```python
import numpy as np
from concrete import fhe

@fhe.compiler({"args": "encrypted"})
def fhe_min(args):
    remaining = list(args)
    while len(remaining) > 1:
        a = remaining.pop()
        b = remaining.pop()
        min_a_b = b - np.maximum(b - a, 0)
        remaining.insert(0, min_a_b)
    return remaining[0]

inputset = [np.random.randint(0, 16, size=5) for _ in range(50)]
circuit = fhe_min.compile(inputset)

x1, x2, x3, x4, x5 = np.random.randint(0, 16, size=5)
assert circuit.encrypt_run_decrypt([x1, x2, x3, x4, x5]) == min(x1, x2, x3, x4, x5)
```

## Retrieving a value within an encrypted array with an encrypted index

This example shows how to deal with an array and an encrypted index. It will create a "selection" array filled with `0` except for the requested index that will be `1`, and sum the products of all array values by this selection array:

```python
import numpy as np
from concrete import fhe

@fhe.compiler({"array": "encrypted", "index": "encrypted"})
def indexed_value(array, index):
    all_indices = np.arange(array.size)
    index_selection = index == all_indices
    selection_and_zeros = array * index_selection
    selection = np.sum(selection_and_zeros)
    return selection

inputset = [(np.random.randint(0, 16, size=5), np.random.randint(0, 5)) for _ in range(50)]
circuit = indexed_value.compile(inputset)

array = np.random.randint(0, 16, size=5)

index = np.random.randint(0, 5)
assert circuit.encrypt_run_decrypt(array, index) == array[index]
```

## Filter an array with comparison (>)

This example filters an encrypted array with an encrypted condition, here a `greater than` with an encrypted value. It packs all values with a selection bit, resulting from the comparison that allow the unpacking of only the filtered values:

```python
import numpy as np
from concrete import fhe

@fhe.compiler({"numbers": "encrypted", "threshold": "encrypted"})
def filtering(numbers, threshold):
    is_greater = numbers > threshold

    shifted_numbers = numbers * 2  # open space for a single bit at the end
    combined_numbers_and_is_greater = shifted_numbers + is_greater  # put is_greater to that bit

    def extract(combination):
        is_greater = (combination % 2) == 1  # extract is_greater back from packing
        if_true = combination // 2  # if is greater is true, we unpack the number and use it
        if_false = 0  # otherwise we set the element to zero
        return np.where(is_greater, if_true, if_false)  # and apply the operation

    return fhe.univariate(extract)(combined_numbers_and_is_greater)

inputset = [(np.random.randint(0, 16, size=5), np.random.randint(0, 16)) for _ in range(50)]
circuit = filtering.compile(inputset)

numbers = np.random.randint(0, 16, size=5)
threshold = np.random.randint(0, 16)
assert np.array_equal(circuit.encrypt_run_decrypt(numbers, threshold), list(map(lambda x: x if x > threshold else 0, numbers)))

```

## Matrix Row/Col means

In this example Matrix operation, we are introducing a key concept when using Concrete: trying to maximize the parallelization. Here instead of sequentially summing all values to create a mean value, we split the values in sub-groups, and do the mean of the sub-group means:

```python
import numpy as np
from concrete import fhe

def smallest_prime_divisor(n):
    if n % 2 == 0:
        return 2

    for i in range(3, int(np.sqrt(n)) + 1):
        if n % i == 0:
            return i

    return n

def mean_of_vector(x):
    assert x.size != 0
    if x.size == 1:
        return x[0]

    group_size = smallest_prime_divisor(x.size)
    if x.size == group_size:
        return np.round(np.sum(x) / x.size).astype(np.int64)

    groups = []
    for i in range(x.size // group_size):
        start = i * group_size
        end = start + group_size
        groups.append(x[start:end])

    mean_of_groups = []
    for group in groups:
        mean_of_groups.append(np.round(np.sum(group) / group_size).astype(np.int64))

    return mean_of_vector(fhe.array(mean_of_groups))

@fhe.compiler(({"x": "encrypted"}))
def mean_of_matrix(x):
    return mean_of_vector(x.flatten())

@fhe.compiler(({"x": "encrypted"}))
def mean_of_rows_of_matrix(x):
    means = []
    for i in range(x.shape[0]):
        means.append(mean_of_vector(x[i]))
    return fhe.array(means)

@fhe.compiler(({"x": "encrypted"}))
def mean_of_columns_of_matrix(x):
    means = []
    for i in range(x.shape[1]):
        means.append(mean_of_vector(x[:, i]))
    return fhe.array(means)


inputset = [np.random.randint(0, 16, size=(5,5)) for _ in range(50)]
matrix = np.random.randint(0, 16, size=(5, 5))

circuit = mean_of_matrix.compile(inputset)
assert circuit.encrypt_run_decrypt(matrix) == round(matrix.mean())

circuit = mean_of_rows_of_matrix.compile(inputset)
assert np.array_equal(circuit.encrypt_run_decrypt(matrix), [round(x) for x in matrix.mean(1)])

circuit = mean_of_columns_of_matrix.compile(inputset)
assert np.array_equal(circuit.encrypt_run_decrypt(matrix), [round(x) for x in matrix.mean(0)])
```
