A more complex script generated by gpt4-turbo
#8
by
win10
- opened
- mathrandom3.py +73 -0
mathrandom3.py
ADDED
@@ -0,0 +1,73 @@
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1 |
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import random
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import json
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import operator
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import math
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# Define the number of samples you want to generate
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num_samples = 500000
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# Define the range for the random numbers
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min_value = -99.99
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max_value = 99.99
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# Define the arithmetic operations and their corresponding functions
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operations = {
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'+': operator.add,
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'-': operator.sub,
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'*': operator.mul,
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'/': operator.truediv,
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'^': operator.pow
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}
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def generate_random_number():
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return float("%.3f" % random.uniform(min_value, max_value))
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def safe_division(numerator, denominator):
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return numerator if denominator == 0 else numerator / denominator
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def safe_power(base, exponent):
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try:
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return math.pow(base, exponent)
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except OverflowError:
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return float('inf') if base > 0 else float('-inf')
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# Function to evaluate expressions safely
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def evaluate_expression(expression):
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try:
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return eval(expression, {"__builtins__": {}}, operations)
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except ZeroDivisionError:
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return None
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# Function to generate a random arithmetic expression
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def generate_expression(depth=0):
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num1 = generate_random_number()
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num2 = generate_random_number()
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operation = random.choice(list(operations.keys()))
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if operation == '/': # Ensure second number is not zero for division
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num2 = num2 or generate_random_number()
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if operation == '^': # Validate power operations
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num1, num2 = min(num1, 10), min(num2, 10) # Limit the values for power operations to prevent overflow
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expression = f"{num1} {operation} {num2}"
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# Add a chance to nest expressions inside parentheses
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if depth < 2 and random.choice([True, False]): # Limit the depth to prevent overly complex expressions
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nested_expression = generate_expression(depth + 1)
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if random.choice([True, False]):
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expression = f"({expression}) {operation} ({nested_expression})"
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else:
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expression = f"({expression}) {operation} {nested_expression}"
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return expression
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# Generate complex data including unary operations and nested expressions
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data = []
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while len(data) < num_samples:
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expression = generate_expression()
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result = evaluate_expression(expression)
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if result is not None: # Only accept expressions that don't lead to division by zero
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output = "%.4f" % result
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data.append({'instruction': expression, 'output': output})
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# Create the dataset
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out_file = 'arithmetic-float-complex.json'
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with open(out_file, 'w') as f:
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json.dump(data, f, indent=2)
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print(f"Generated {len(data)} samples.")
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