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## to Saddle Points in the Python Track

Published at Jul 21 2018 · 0 comments
Instructions
Test suite
Solution

#### Note:

This exercise has changed since this solution was written.

Detect saddle points in a matrix.

So say you have a matrix like so:

``````    0  1  2
|---------
0 | 9  8  7
1 | 5  3  2     <--- saddle point at (1,0)
2 | 6  6  7
``````

It has a saddle point at (1, 0).

It's called a "saddle point" because it is greater than or equal to every element in its row and less than or equal to every element in its column.

A matrix may have zero or more saddle points.

Your code should be able to provide the (possibly empty) list of all the saddle points for any given matrix.

Note that you may find other definitions of matrix saddle points online, but the tests for this exercise follow the above unambiguous definition.

## Exception messages

Sometimes it is necessary to raise an exception. When you do this, you should include a meaningful error message to indicate what the source of the error is. This makes your code more readable and helps significantly with debugging. Not every exercise will require you to raise an exception, but for those that do, the tests will only pass if you include a message.

To raise a message with an exception, just write it as an argument to the exception type. For example, instead of `raise Exception`, you should write:

``````raise Exception("Meaningful message indicating the source of the error")
``````

## Running the tests

To run the tests, run the appropriate command below (why they are different):

• Python 2.7: `py.test saddle_points_test.py`
• Python 3.4+: `pytest saddle_points_test.py`

Alternatively, you can tell Python to run the pytest module (allowing the same command to be used regardless of Python version): `python -m pytest saddle_points_test.py`

### Common `pytest` options

• `-v` : enable verbose output
• `-x` : stop running tests on first failure
• `--ff` : run failures from previous test before running other test cases

For other options, see `python -m pytest -h`

## Submitting Exercises

Note that, when trying to submit an exercise, make sure the solution is in the `\$EXERCISM_WORKSPACE/python/saddle-points` directory.

You can find your Exercism workspace by running `exercism debug` and looking for the line that starts with `Workspace`.

For more detailed information about running tests, code style and linting, please see Running the Tests.

## Source

J Dalbey's Programming Practice problems http://users.csc.calpoly.edu/~jdalbey/103/Projects/ProgrammingPractice.html

## Submitting Incomplete Solutions

It's possible to submit an incomplete solution so you can see how others have completed the exercise.

``````"""Tests for the saddle-points exercise

Implementation note:
The saddle_points function must validate the input matrix and raise a
ValueError with a meaningful error message if the matrix turns out to be
irregular.
"""
import unittest

# Tests adapted from `problem-specifications//canonical-data.json` @ v1.1.0

matrix = [[9, 8, 7], [5, 3, 2], [6, 6, 7]]

matrix = [[1, 2, 3], [3, 1, 2], [2, 3, 1]]

matrix = [[4, 5, 4], [3, 5, 5], [1, 5, 4]]
expected = set([(0, 1), (1, 1), (2, 1)])

matrix = [[8, 7, 9], [6, 7, 6], [3, 2, 5]]
expected = set([(2, 2)])

# Additional tests for this track

def test_irregular_matrix(self):
matrix = [[3, 2, 1], [0, 1], [2, 1, 0]]
with self.assertRaisesWithMessage(ValueError):

# Utility functions
def setUp(self):
try:
self.assertRaisesRegex
except AttributeError:
self.assertRaisesRegex = self.assertRaisesRegexp

def assertRaisesWithMessage(self, exception):
return self.assertRaisesRegex(exception, r".+")

if __name__ == '__main__':
unittest.main()``````
``````def filter_check(function, xs):
for x in xs:
if function(x):
pass
else:
return False
return True

def verify_matrix(matrix):
n_rows = len(matrix)
for row in matrix:
if len(row) != n_rows:
raise ValueError('irregular matrix')

verify_matrix(matrix)
size = len(matrix)
points = []
for i in range(size):
for j in range(size):
points.append((i, j))

for (x, y) in points:
row_values = matrix[x]
col_values = [matrix[k][y] for k in range(0, size)]
pt_value = matrix[x][y]
good_row = filter_check(lambda p: p <= pt_value, row_values)
good_col = filter_check(lambda p: p >= pt_value, col_values)
if good_row and good_col: