What Gives a Bad Mood

Challenge Difficulty: Medium | Estimated completion time: ~30 minutes

Let’s say the greatest impact on someone’s mood are: weather, meals, and sleep.

Your task is, given an array of sub-arrays of different values for:

[Mood, Weather, Meals, Sleep].

All values except for meals are 1-10 (1 = bad, 10 = good)

Meals are from 1-3

Determine which other variable has had the greatest impact on the mood.

Examples

greatestImpact([
  [1, 1, 3, 10],
  [1, 1, 3, 10],
  [1, 1, 3, 10]
])
output = "Weather"
# Weather was always low but all others were high.
 
greatestImpact([
  [10, 10, 3, 10],
  [10, 10, 3, 10],
  [10, 10, 3, 10]
])
output = "Nothing"
 
# Great days! all values were high.
 
greatestImpact([
  [8, 9, 3, 10],
  [2, 10, 1, 9],
  [1, 9, 1, 8]
])
output = "Meals"
 
greatestImpact([
  [10, 9, 3, 9],
  [1, 8, 3, 4],
  [10, 9, 2, 8],
  [2, 9, 3, 2]
])
output = "Sleep"

Notes

• All values except for meals are 1-10 (1 = bad, 10 = good)
• Meals are from 1-3

Solution

Python

def greatestImpact(data: list[list[int]]) -> str: """ Determine which factor has the greatest impact on mood. Analyzes correlations between mood and weather, meals, and sleep to find which factor most strongly correlates with mood changes. Args: data: List of [Mood, Weather, Meals, Sleep] sublists. Mood, Weather, Sleep are 1-10. Meals are 1-3. Returns: String indicating which factor has greatest impact: 'Weather', 'Meals', 'Sleep', or 'Nothing'. Examples: >>> greatestImpact([[1, 1, 3, 10], [1, 1, 3, 10], [1, 1, 3, 10]]) 'Weather' """ if not data: return "Nothing" # Calculate correlation for each factor moods = [entry[0] for entry in data] weather_values = [entry[1] for entry in data] meals_values = [entry[2] for entry in data] sleep_values = [entry[3] for entry in data] def calculate_correlation(mood_list: list[int], factor_list: list[int]) -> float: """Calculate simple correlation between mood and a factor.""" if len(mood_list) != len(factor_list): return 0.0 # Calculate means mood_mean = sum(mood_list) / len(mood_list) factor_mean = sum(factor_list) / len(factor_list) # Calculate covariance and standard deviations covariance = sum((mood_list[i] - mood_mean) * (factor_list[i] - factor_mean) for i in range(len(mood_list))) mood_variance = sum((mood - mood_mean) ** 2 for mood in mood_list) factor_variance = sum((factor - factor_mean) ** 2 for factor in factor_list) if mood_variance <span class="text-highlight"> 0 or factor_variance </span> 0: return 0.0 correlation = covariance / ((mood_variance * factor_variance) ** 0.5) return abs(correlation) weather_correlation = calculate_correlation(moods, weather_values) meals_correlation = calculate_correlation(moods, meals_values) sleep_correlation = calculate_correlation(moods, sleep_values) max_correlation = max(weather_correlation, meals_correlation, sleep_correlation) # Threshold for considering something as having an impact if max_correlation < 0.1: return "Nothing" if weather_correlation <span class="text-highlight"> max_correlation: return "Weather" elif meals_correlation </span> max_correlation: return "Meals" else: return "Sleep" if __name__ <span class="text-highlight"> "__main__": # Test cases assert greatestImpact([[1, 1, 3, 10], [1, 1, 3, 10], [1, 1, 3, 10]]) </span> "Weather" assert greatestImpact([[10, 10, 3, 10], [10, 10, 3, 10], [10, 10, 3, 10]]) <span class="text-highlight"> "Nothing" assert greatestImpact([[8, 9, 3, 10], [2, 10, 1, 9], [1, 9, 1, 8]]) </span> "Meals" assert greatestImpact([[10, 9, 3, 9], [1, 8, 3, 4], [10, 9, 2, 8], [2, 9, 3, 2]]) == "Sleep" print("All test cases passed!")

Output