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Bum Simulator Download For Pcl



 


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## Setup Set up our environment ```sh conda create -n ml_kaggle python=3.6.5 conda activate ml_kaggle ``` ## Loading the training data We have two CSV files that we will read to our training data. mkdir -p data python -c "from os.path import join, splitext; file_1 = join(splitext(__file__), 'train_data_csv_1.csv'); file_2 = join(splitext(__file__), 'train_data_csv_2.csv');" ## Preparing the text data. We'll prepare the data so that we can properly run our preprocessing steps on it. python -c "import re; text_data = open('data/train_text_data.txt', 'r'); print(text_data.read())""" Then remove any headers python -c "import numpy as np; headers = np.array(text_data.readlines())[0:-1]; print(headers.shape)" Now we split the data into two columns, the features and the target python -c "import csv; features, target = np.loadtxt(text_data, delimiter=','); print(features.shape)" ## Preparing the tagging data. For the tagging data we'll use a spreadsheet and we'll split the data into features and target. python -c "import csv; csv_file = open('data/tagging_data_csv_1.csv', 'r'); print(csv_file.readlines())""" python -c "import numpy as np; headers = np.array(csv_file.readlines())[0:-1]; print(headers.shape)" Finally split the data into two columns, the features and the target python -c "import csv; tags = np.array(csv_file.readlines())[1:-1]; features,

 

 

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Bum Simulator Download For Pcl
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