Lucas Oskorep
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
from glob import glob
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import numpy as np
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import scipy as sp
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import pandas as pd
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from sklearn.cluster import KMeans
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from sklearn.metrics import accuracy_score, confusion_matrix
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import os
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from sklearn.ensemble import RandomForestClassifier
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og_data_columns = 4
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def apply_k_means_clustering(data, kmeans):
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hist = np.zeros((len(kmeans.cluster_centers_),))
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for x in kmeans.predict(data):
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hist[x]+=1
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print(hist)
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return hist
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def quantize_flattened_data(data, length):
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# print("quantizing the data")
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# print(data)
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quantile_length = length*og_data_columns
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rows = int(len(data)/quantile_length)
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return np.resize(data, (rows, quantile_length))
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def load_data(data_dir):
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data = []
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for root, dirs, files in os.walk(data_dir):
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if root is data_dir:
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continue
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for file in files:
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data.append([os.path.basename(root), pd.read_csv(os.path.join(root, file)).values.flatten()])
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return pd.DataFrame(data, columns=["exercise", "flat_data"])
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train_data = load_data("./train")
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test_data = load_data("./test")
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# val_data = load_data("./val")
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quant_len = 3
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train_data["quantized_data"] = train_data["flat_data"].apply(lambda x : quantize_flattened_data(x, quant_len))
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test_data["quantized_data"] = test_data["flat_data"].apply(lambda x : quantize_flattened_data(x, quant_len))
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linked_data = train_data["quantized_data"][0]
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for x in train_data["quantized_data"]:
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linked_data = np.append(linked_data, x, axis=0)
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print(linked_data)
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k_means_model = KMeans(n_clusters=25, n_init=15, max_iter= 500).fit(linked_data)
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print(k_means_model.cluster_centers_)
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train_data["histogram"] = train_data["quantized_data"].apply(lambda x: apply_k_means_clustering(x, k_means_model))
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test_data["histogram"] = test_data["quantized_data"].apply(lambda x: apply_k_means_clustering(x, k_means_model))
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print(train_data)
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clf = RandomForestClassifier(n_estimators=50)
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print(train_data["histogram"].values)
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clf.fit([x for x in train_data["histogram"]], train_data["exercise"].values)
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results = clf.predict([x for x in test_data["histogram"]])
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print(results)
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print(test_data["exercise"].values)
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print(accuracy_score( test_data["exercise"].values,results ))
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print(confusion_matrix( test_data["exercise"].values,results))
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