imagination_of_word / dataset_generator_program.py

adarsh ajay

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import pandas as pd
import numpy as np

# Define the number of samples
num_samples = 1000

# Generate random data for GPU, RAM, and Processor
gpu = np.random.randint(1, 11, size=num_samples)  # Assuming GPU from 1 to 10
ram = np.random.randint(4, 33, size=num_samples)  # Assuming RAM from 4GB to 32GB
processor = np.random.randint(1, 9, size=num_samples)  # Assuming processor cores from 1 to 8

# Calculate whether it is good for a transformer to run (binary: 0 or 1)
# You would replace this logic with your actual criteria for determining suitability
def is_good_for_transformer(gpu, ram, processor):
    return ((gpu >= 6) & (ram >= 16) & (processor >= 4)).astype(int)

output = is_good_for_transformer(gpu, ram, processor)

# Create a DataFrame
data = pd.DataFrame({'GPU': gpu, 'RAM': ram, 'Processor': processor, 'Good_for_Transformer': output})

# Save the DataFrame to a CSV file
data.to_csv(r'Data_csv\transformer_dataset.csv', index=False)

	import pandas as pd
	import numpy as np

	# Define the number of samples
	num_samples = 1000

	# Generate random data for GPU, RAM, and Processor
	gpu = np.random.randint(1, 11, size=num_samples) # Assuming GPU from 1 to 10
	ram = np.random.randint(4, 33, size=num_samples) # Assuming RAM from 4GB to 32GB
	processor = np.random.randint(1, 9, size=num_samples) # Assuming processor cores from 1 to 8

	# Calculate whether it is good for a transformer to run (binary: 0 or 1)
	# You would replace this logic with your actual criteria for determining suitability
	def is_good_for_transformer(gpu, ram, processor):
	return ((gpu >= 6) & (ram >= 16) & (processor >= 4)).astype(int)

	output = is_good_for_transformer(gpu, ram, processor)

	# Create a DataFrame
	data = pd.DataFrame({'GPU': gpu, 'RAM': ram, 'Processor': processor, 'Good_for_Transformer': output})

	# Save the DataFrame to a CSV file
	data.to_csv(r'Data_csv\transformer_dataset.csv', index=False)