<jupyter_start><jupyter_text>Action Receptive Fields<jupyter_code>rf_weights_actions=compute_rfield(imgs,action_one_hot_arr.T)
print(rf_weights_actions.shape)
rf_weights_actions=rf_weights_actions.reshape(84*84,-1)<jupyter_output><empty_output><jupyter_text>PCA<jupyter_code>V=fit_pca(ts)

rf_weights_pca=compute_rfield(imgs,V)
rf_weights_pca=rf_weights_pca.reshape(84*84,-1)<jupyter_output><empty_output><jupyter_text>Correlation<jupyter_code>corr_arr=[]
for j in range(0,100):
    V_corrs=[]
    for i in range(0,5):
        V_corrs.append(np.corrcoef(rf_weights_pca[:,j],rf_weights_actions[:,i])[0,1])
    corr_arr.append(V_corrs)
corr_arr=np.array(corr_arr)
import seaborn as sns
sns.heatmap(corr_arr)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13197278911564625, 'fraction_numerical': 0.024489795918367346, 'mean_word_length': 12.142857142857142, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '375820', 'license': 'Apache-2.0', 'n_tokens_mistral': 338, 'n_tokens_neox': 321, 'n_words': 24, 'path': 'dev/CorrelatingReceptiveFields.ipynb', 'repo_name': 'mariakesa/UdacityMachineLearningEngineerNanoDegree', 'size': '735'}