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Expand Up@@ -518,6 +518,7 @@
* [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
* [Activity Selection](greedy_methods/activity_selection.py)
* [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
* [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
* [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
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52 changes: 52 additions & 0 deletions greedy_methods/activity_selection.py
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"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
"""
Solves the Activity Selection Problem using a greedy algorithm by selecting
the maximum number of non-overlapping activities from a list of activities.

Parameters:
activities (list[tuple[int, int]]): A list of tuples where each tuple contains
the start and end times of an activity.

Returns:
list[tuple[int, int]]: A list of selected activities that are non-overlapping.

Example:
>>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
[(1, 3), (6, 8)]

>>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
[(1, 4), (5, 7), (8, 9)]

>>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
[(1, 2), (2, 4)]

>>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
[(1, 2), (3, 4), (5, 9)]
"""

# Step 1: Sort the activities by their end time
sorted_activities = sorted(activities, key=lambda x: x[1])

# Step 2: Select the first activity (the one that finishes the earliest)
# as the initial activity
selected_activities = [sorted_activities[0]]

# Step 3: Iterate through the sorted activities and select the ones
# that do not overlap with the last selected activity
for i in range(1, len(sorted_activities)):
if sorted_activities[i][0] >= selected_activities[-1][1]:
selected_activities.append(sorted_activities[i])

return selected_activities
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function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
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var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
Add activity selection algorithm by Hardvan · Pull Request #11591 · TheAlgorithms/Python · GitHub
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Expand Up@@ -518,6 +518,7 @@
 * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
 * [Activity Selection](greedy_methods/activity_selection.py)
 * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
 * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
 * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
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greedy_methods/activity_selection.py

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"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
 """
 Solves the Activity Selection Problem using a greedy algorithm by selecting
 the maximum number of non-overlapping activities from a list of activities.

 Parameters:
 activities (list[tuple[int, int]]): A list of tuples where each tuple contains
 the start and end times of an activity.

 Returns:
 list[tuple[int, int]]: A list of selected activities that are non-overlapping.

 Example:
 >>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
 [(1, 3), (6, 8)]

 >>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
 [(1, 4), (5, 7), (8, 9)]

 >>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
 [(1, 2), (2, 4)]

 >>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
 [(1, 2), (3, 4), (5, 9)]
 """

 # Step 1: Sort the activities by their end time
 sorted_activities = sorted(activities, key=lambda x: x[1])

 # Step 2: Select the first activity (the one that finishes the earliest)
 # as the initial activity
 selected_activities = [sorted_activities[0]]

 # Step 3: Iterate through the sorted activities and select the ones
 # that do not overlap with the last selected activity
 for i in range(1, len(sorted_activities)):
 if sorted_activities[i][0] >= selected_activities[-1][1]:
 selected_activities.append(sorted_activities[i])

 return selected_activities

, 'i');
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// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
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color-scheme: dark light;
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var __re = new RegExp('^' + ".*" + '
Add activity selection algorithm by Hardvan · Pull Request #11591 · TheAlgorithms/Python · GitHub
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Add activity selection algorithm#11591

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Expand Up@@ -518,6 +518,7 @@
 * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
 * [Activity Selection](greedy_methods/activity_selection.py)
 * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
 * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
 * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
Expand Down

52 changes: 52 additions & 0 deletions
greedy_methods/activity_selection.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,52 @@
"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
 """
 Solves the Activity Selection Problem using a greedy algorithm by selecting
 the maximum number of non-overlapping activities from a list of activities.

 Parameters:
 activities (list[tuple[int, int]]): A list of tuples where each tuple contains
 the start and end times of an activity.

 Returns:
 list[tuple[int, int]]: A list of selected activities that are non-overlapping.

 Example:
 >>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
 [(1, 3), (6, 8)]

 >>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
 [(1, 4), (5, 7), (8, 9)]

 >>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
 [(1, 2), (2, 4)]

 >>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
 [(1, 2), (3, 4), (5, 9)]
 """

 # Step 1: Sort the activities by their end time
 sorted_activities = sorted(activities, key=lambda x: x[1])

 # Step 2: Select the first activity (the one that finishes the earliest)
 # as the initial activity
 selected_activities = [sorted_activities[0]]

 # Step 3: Iterate through the sorted activities and select the ones
 # that do not overlap with the last selected activity
 for i in range(1, len(sorted_activities)):
 if sorted_activities[i][0] >= selected_activities[-1][1]:
 selected_activities.append(sorted_activities[i])

 return selected_activities

, 'i');
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// Highlight search terms from Google/DuckDuckGo/Bing referrer
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var ref = document.referrer;
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(function(){
try {
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var __re = new RegExp('^' + ".*" + '
Add activity selection algorithm by Hardvan · Pull Request #11591 · TheAlgorithms/Python · GitHub
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DIRECTORY.md

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Expand Up@@ -518,6 +518,7 @@
 * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
 * [Activity Selection](greedy_methods/activity_selection.py)
 * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
 * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
 * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
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greedy_methods/activity_selection.py

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@@ -0,0 +1,52 @@
"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
 """
 Solves the Activity Selection Problem using a greedy algorithm by selecting
 the maximum number of non-overlapping activities from a list of activities.

 Parameters:
 activities (list[tuple[int, int]]): A list of tuples where each tuple contains
 the start and end times of an activity.

 Returns:
 list[tuple[int, int]]: A list of selected activities that are non-overlapping.

 Example:
 >>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
 [(1, 3), (6, 8)]

 >>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
 [(1, 4), (5, 7), (8, 9)]

 >>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
 [(1, 2), (2, 4)]

 >>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
 [(1, 2), (3, 4), (5, 9)]
 """

 # Step 1: Sort the activities by their end time
 sorted_activities = sorted(activities, key=lambda x: x[1])

 # Step 2: Select the first activity (the one that finishes the earliest)
 # as the initial activity
 selected_activities = [sorted_activities[0]]

 # Step 3: Iterate through the sorted activities and select the ones
 # that do not overlap with the last selected activity
 for i in range(1, len(sorted_activities)):
 if sorted_activities[i][0] >= selected_activities[-1][1]:
 selected_activities.append(sorted_activities[i])

 return selected_activities

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
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var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
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try {
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if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
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});
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} catch (e) {
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if (clean !== a.href) a.href = clean;
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cleanLinks();
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
Add activity selection algorithm by Hardvan · Pull Request #11591 · TheAlgorithms/Python · GitHub
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DIRECTORY.md

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Expand Up@@ -518,6 +518,7 @@
 * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
 * [Activity Selection](greedy_methods/activity_selection.py)
 * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
 * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
 * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
Expand Down

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greedy_methods/activity_selection.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,52 @@
"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
 """
 Solves the Activity Selection Problem using a greedy algorithm by selecting
 the maximum number of non-overlapping activities from a list of activities.

 Parameters:
 activities (list[tuple[int, int]]): A list of tuples where each tuple contains
 the start and end times of an activity.

 Returns:
 list[tuple[int, int]]: A list of selected activities that are non-overlapping.

 Example:
 >>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
 [(1, 3), (6, 8)]

 >>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
 [(1, 4), (5, 7), (8, 9)]

 >>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
 [(1, 2), (2, 4)]

 >>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
 [(1, 2), (3, 4), (5, 9)]
 """

 # Step 1: Sort the activities by their end time
 sorted_activities = sorted(activities, key=lambda x: x[1])

 # Step 2: Select the first activity (the one that finishes the earliest)
 # as the initial activity
 selected_activities = [sorted_activities[0]]

 # Step 3: Iterate through the sorted activities and select the ones
 # that do not overlap with the last selected activity
 for i in range(1, len(sorted_activities)):
 if sorted_activities[i][0] >= selected_activities[-1][1]:
 selected_activities.append(sorted_activities[i])

 return selected_activities

, 'i');
if (__m === '*' || __re.test(location.href)) {
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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setTimeout(tryTheater, 500);
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}, 1000);
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observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Add activity selection algorithm by Hardvan · Pull Request #11591 · TheAlgorithms/Python · GitHub
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Add activity selection algorithm#11591

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Add activity selection algorithm
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Add activity selection algorithm


Hardvan Sep 30, 2024
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updating DIRECTORY.md


Hardvan Sep 30, 2024

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DIRECTORY.md

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Expand Up@@ -518,6 +518,7 @@
 * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
 * [Activity Selection](greedy_methods/activity_selection.py)
 * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
 * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
 * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
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greedy_methods/activity_selection.py

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"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
 """
 Solves the Activity Selection Problem using a greedy algorithm by selecting
 the maximum number of non-overlapping activities from a list of activities.

 Parameters:
 activities (list[tuple[int, int]]): A list of tuples where each tuple contains
 the start and end times of an activity.

 Returns:
 list[tuple[int, int]]: A list of selected activities that are non-overlapping.

 Example:
 >>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
 [(1, 3), (6, 8)]

 >>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
 [(1, 4), (5, 7), (8, 9)]

 >>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
 [(1, 2), (2, 4)]

 >>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
 [(1, 2), (3, 4), (5, 9)]
 """

 # Step 1: Sort the activities by their end time
 sorted_activities = sorted(activities, key=lambda x: x[1])

 # Step 2: Select the first activity (the one that finishes the earliest)
 # as the initial activity
 selected_activities = [sorted_activities[0]]

 # Step 3: Iterate through the sorted activities and select the ones
 # that do not overlap with the last selected activity
 for i in range(1, len(sorted_activities)):
 if sorted_activities[i][0] >= selected_activities[-1][1]:
 selected_activities.append(sorted_activities[i])

 return selected_activities

, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
Add activity selection algorithm by Hardvan · Pull Request #11591 · TheAlgorithms/Python · GitHub
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Add activity selection algorithm#11591

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Add activity selection algorithm
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Add activity selection algorithm


Hardvan Sep 30, 2024
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updating DIRECTORY.md


Hardvan Sep 30, 2024

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DIRECTORY.md

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Expand Up@@ -518,6 +518,7 @@
 * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
 * [Activity Selection](greedy_methods/activity_selection.py)
 * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
 * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
 * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
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greedy_methods/activity_selection.py

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@@ -0,0 +1,52 @@
"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
 """
 Solves the Activity Selection Problem using a greedy algorithm by selecting
 the maximum number of non-overlapping activities from a list of activities.

 Parameters:
 activities (list[tuple[int, int]]): A list of tuples where each tuple contains
 the start and end times of an activity.

 Returns:
 list[tuple[int, int]]: A list of selected activities that are non-overlapping.

 Example:
 >>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
 [(1, 3), (6, 8)]

 >>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
 [(1, 4), (5, 7), (8, 9)]

 >>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
 [(1, 2), (2, 4)]

 >>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
 [(1, 2), (3, 4), (5, 9)]
 """

 # Step 1: Sort the activities by their end time
 sorted_activities = sorted(activities, key=lambda x: x[1])

 # Step 2: Select the first activity (the one that finishes the earliest)
 # as the initial activity
 selected_activities = [sorted_activities[0]]

 # Step 3: Iterate through the sorted activities and select the ones
 # that do not overlap with the last selected activity
 for i in range(1, len(sorted_activities)):
 if sorted_activities[i][0] >= selected_activities[-1][1]:
 selected_activities.append(sorted_activities[i])

 return selected_activities

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
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var enabled = true;
function applyDarkMode() {
if (!enabled) return;
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if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
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/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
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::-webkit-scrollbar-thumb:hover { background: #555 !important; }
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::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
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document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
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console.log('[Universal Dark Mode] Disabled');
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}
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// Re-apply on dynamic content
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if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
Add activity selection algorithm by Hardvan · Pull Request #11591 · TheAlgorithms/Python · GitHub
Skip to content
Add activity selection algorithm#11591

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 Closed

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Add activity selection algorithm
#11591
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11d1e4c

Add activity selection algorithm


Hardvan Sep 30, 2024
1c0e3f7

updating DIRECTORY.md


Hardvan Sep 30, 2024

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DIRECTORY.md

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Original file line numberDiff line numberDiff line change
Expand Up@@ -518,6 +518,7 @@
 * [Test Min Spanning Tree Prim](graphs/tests/test_min_spanning_tree_prim.py)

## Greedy Methods
 * [Activity Selection](greedy_methods/activity_selection.py)
 * [Best Time To Buy And Sell Stock](greedy_methods/best_time_to_buy_and_sell_stock.py)
 * [Fractional Cover Problem](greedy_methods/fractional_cover_problem.py)
 * [Fractional Knapsack](greedy_methods/fractional_knapsack.py)
Expand Down

52 changes: 52 additions & 0 deletions
greedy_methods/activity_selection.py

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,52 @@
"""
The Activity Selection Problem is a classic problem in which a set of activities,
each with a start and end time, needs to be scheduled in such a way that
the maximum number of non-overlapping activities is selected.
This is a greedy algorithm where at each step,
we choose the activity that finishes the earliest
and does not conflict with previously selected activities.

Wikipedia: https://en.wikipedia.org/wiki/Activity_selection_problem
"""


def activity_selection(activities: list[tuple[int, int]]) -> list[tuple[int, int]]:
 """
 Solves the Activity Selection Problem using a greedy algorithm by selecting
 the maximum number of non-overlapping activities from a list of activities.

 Parameters:
 activities (list[tuple[int, int]]): A list of tuples where each tuple contains
 the start and end times of an activity.

 Returns:
 list[tuple[int, int]]: A list of selected activities that are non-overlapping.

 Example:
 >>> activity_selection([(1, 3), (2, 5), (3, 9), (6, 8)])
 [(1, 3), (6, 8)]

 >>> activity_selection([(0, 6), (1, 4), (3, 5), (5, 7), (5, 9), (8, 9)])
 [(1, 4), (5, 7), (8, 9)]

 >>> activity_selection([(1, 2), (2, 4), (3, 5), (0, 6)])
 [(1, 2), (2, 4)]

 >>> activity_selection([(5, 9), (1, 2), (3, 4), (0, 6)])
 [(1, 2), (3, 4), (5, 9)]
 """

 # Step 1: Sort the activities by their end time
 sorted_activities = sorted(activities, key=lambda x: x[1])

 # Step 2: Select the first activity (the one that finishes the earliest)
 # as the initial activity
 selected_activities = [sorted_activities[0]]

 # Step 3: Iterate through the sorted activities and select the ones
 # that do not overlap with the last selected activity
 for i in range(1, len(sorted_activities)):
 if sorted_activities[i][0] >= selected_activities[-1][1]:
 selected_activities.append(sorted_activities[i])

 return selected_activities