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Functions1,092 in github.com/csawtelle/udemy-machine-learning-examples

⨍ Functions 1,092◇ Types & classes 147
↓ 4 callersMethodscore
(self, X, Y)
airline/ann.py:124
↓ 4 callersFunctionto_bin
(value, bins)
rl2/cartpole/q_learning_bins.py:25
↓ 4 callersMethodupdate
(self, s, a, G, gamma, lambda_)
rl2/mountaincar/td_lambda.py:57
↓ 4 callersFunctiony2indicator
(y)
rnn_class/renet.py:32
↓ 3 callersFunctionadd2dict
(d, k, v)
hmm_class/frost.py:16
↓ 3 callersFunctionall_parity_pairs
(nbit)
rnn_class/util.py:13
↓ 3 callersFunctionbig_init
()
hmm_class/generate_c.py:26
↓ 3 callersFunctiond
(u, v)
unsupervised_class/kmeans_visualize.py:7
↓ 3 callersMethoddraw_board
(self)
rl/tic_tac_toe.py:229
↓ 3 callersFunctionentropy
(y)
supervised_class/dt.py:9
↓ 3 callersFunctionerror_rate
(p, t)
unsupervised_class2/util.py:12
↓ 3 callersFunctionf
(X)
supervised_class2/bias_variance_demo.py:26
↓ 3 callersMethodfit
(self, X, max_iter=30, eps=1e0)
hmm_class/hmmc.py:22
↓ 3 callersMethodfit
(self, X, Y, activation=T.nnet.relu, learning_rate=1e-3, mu=0.0, reg=0, epochs=100, batch_sz=None, print_perio
ann_class2/theano_ann.py:39
↓ 3 callersMethodfit
(self, X, Y, V=None, K=None, D=50, lr=1e-1, mu=0.99, batch_sz=100, epochs=6)
nlp_class2/pos_baseline.py:21
↓ 3 callersMethodfit
(self, X, Y, M=None)
supervised_class2/rf_vs_bag2.py:34
↓ 3 callersMethodfit
(self, X)
unsupervised_class3/dcgan_tf.py:405
↓ 3 callersMethodfit
(self, X, learning_rate=0.5, mu=0.99, epochs=50, batch_sz=100, show_fig=False)
unsupervised_class2/xwing.py:40
↓ 3 callersMethodfit
(self, X, Y, Xtest, Ytest, pretrain=True, learning_rate=0.01, mu=0.99, reg=0.1, epochs=1, batch_sz=100)
unsupervised_class2/autoencoder.py:123
↓ 3 callersMethodfit
(self, X, Y, learning_rate=1.0, epochs=1000)
supervised_class/perceptron.py:24
↓ 3 callersFunctionforward
(X, W1, b1, W2, b2)
ann_class/xor_donut.py:13
↓ 3 callersMethodforward_hidden
(self, X)
unsupervised_class2/autoencoder_tf.py:79
↓ 3 callersMethodgame_over
(self, force_recalculate=False)
rl/tic_tac_toe.py:171
↓ 3 callersFunctiongetQs
(model, s)
rl/approx_semigradient_sarsa_control.py:67
↓ 3 callersMethodget_cost_multi
(self, X)
hmm_class/hmmd_tf.py:53
↓ 3 callersMethodget_cost_multi
(self, X, p_cost=1.0)
hmm_class/hmmd_theano.py:95
↓ 3 callersMethodget_cost_multi
(self, X)
hmm_class/hmmd_theano2.py:89
↓ 3 callersMethodget_cost_multi
(self, X)
hmm_class/hmmc_tf.py:167
↓ 3 callersFunctionget_data
(split_sequences=False)
nlp_class2/pos_baseline.py:86
↓ 3 callersFunctionget_data
()
supervised_class2/rf_classification.py:80
↓ 3 callersFunctionget_r2
(X, Y)
linear_regression_class/systolic.py:34
↓ 3 callersMethodget_state
(self)
rl/tic_tac_toe.py:151
↓ 3 callersMethodget_state_sequence
(self, x)
hmm_class/hmmd.py:156
↓ 3 callersFunctiongmm
(X, K, max_iter=20, smoothing=10e-3)
unsupervised_class/gmm.py:9
↓ 3 callersMethodlog_likelihood_multi
(self, X)
hmm_class/hmmc_theano.py:158
↓ 3 callersMethodlog_likelihood_multi
(self, X)
hmm_class/hmmc_theano2.py:166
↓ 3 callersFunctionplot_cost_to_go
(env, estimator, num_tiles=20)
rl2/mountaincar/q_learning.py:120
↓ 3 callersFunctionplot_image
(x, Q, title)
bayesian_ml/2/probit.py:92
↓ 3 callersFunctionplot_running_avg
(totalrewards)
rl2/mountaincar/q_learning.py:140
↓ 3 callersMethodpredict
(self, X)
supervised_class2/util.py:42
↓ 3 callersMethodpredict
(self, X)
supervised_class2/bagging_regression.py:48
↓ 3 callersMethodpredict
(self, X)
bayesian_ml/2/probit.py:55
↓ 3 callersMethodpredict
(self, X)
bayesian_ml/1/nb.py:72
↓ 3 callersMethodpredict
(self, s)
rl2/mountaincar/q_learning.py:76
↓ 3 callersMethodpredict
(self, X)
rl2/cartpole/pg_tf.py:150
↓ 3 callersMethodpredict
(self, s)
rl/approx_semigradient_td0_prediction.py:18
↓ 3 callersMethodpredict
(self, X)
airline/rnn.py:109
↓ 3 callersFunctionrelu
(a)
cnn_class/cnn_theano.py:24
↓ 3 callersFunctionrelu
(a)
rnn_class/renet.py:28
↓ 3 callersFunctionrenet_layer_lr_allscan
(X, rnn1, rnn2, w, h, wp, hp)
rnn_class/renet.py:168
↓ 3 callersFunctionrun
(num=1, T=500)
bayesian_ml/3/run.py:80
↓ 3 callersFunctionrun_experiment
(m1, m2, m3, eps, N)
rl/comparing_epsilons.py:26
↓ 3 callersMethodsample_action
(self, X)
rl2/cartpole/pg_tf.py:106
↓ 3 callersFunctionsample_word
(d)
hmm_class/frost.py:67
↓ 3 callersMethodscore
(self, X, Y)
supervised_class2/rf_vs_bag2.py:63
↓ 3 callersMethodset_symbol
(self, sym)
rl/tic_tac_toe.py:36
↓ 3 callersFunctiontensor_mul
(d, x1, A, x2)
nlp_class2/rntn_tensorflow.py:15
↓ 2 callersMethod_get_negative_samples
(self, context, num_neg_samples)
nlp_class2/word2vec.py:53
↓ 2 callersMethod_get_pnw
(self, X)
nlp_class2/word2vec.py:36
↓ 2 callersFunctionaccuracy
(T, Y)
nlp_class2/pos_hmm.py:18
↓ 2 callersFunctionadam
(params, grads)
unsupervised_class3/dcgan_theano.py:43
↓ 2 callersFunctionadd_idx_to_tree
(tree, current_idx)
nlp_class2/recursive_theano.py:174
↓ 2 callersFunctionadd_idx_to_tree
(tree, current_idx)
nlp_class2/rntn_theano.py:172
↓ 2 callersMethodbuild
(self, D, K)
ann_class2/tf_with_save.py:24
↓ 2 callersMethodbuild
(self, We, Wx, Wh, bh, h0, Wo, bo)
rnn_class/srn_language_tf.py:22
↓ 2 callersFunctionclassification_rate
(Y, P)
ann_logistic_extra/logistic_softmax_train.py:44
↓ 2 callersFunctionclassification_rate
(Y, P)
ann_logistic_extra/ann_train.py:48
↓ 2 callersFunctionclassification_rate
(Y, P)
ann_logistic_extra/logistic_train.py:29
↓ 2 callersFunctionconvolve_flatten
(X)
cnn_class/edge_benchmark.py:34
↓ 2 callersFunctionconvpool
(X, W, b)
cnn_class/cnn_tf.py:26
↓ 2 callersFunctioncost
(T, Y)
ann_class/xor_donut.py:56
↓ 2 callersFunctioncost
(X, R, M)
unsupervised_class/kmeans.py:12
↓ 2 callersFunctioncross_entropy
(T, Y)
logistic_regression_class/logistic2.py:42
↓ 2 callersFunctioncross_entropy
(T, pY)
ann_logistic_extra/logistic_softmax_train.py:47
↓ 2 callersFunctioncross_entropy
(T, pY)
ann_logistic_extra/ann_train.py:51
↓ 2 callersFunctioncross_entropy
(T, pY)
ann_logistic_extra/logistic_train.py:33
↓ 2 callersMethodd_forward
(self, X, reuse=None, is_training=True)
unsupervised_class3/dcgan_tf.py:299
↓ 2 callersMethodd_forward
(self, X, is_training)
unsupervised_class3/dcgan_theano.py:354
↓ 2 callersFunctionderivative_b1
(Z, T, Y, W2)
ann_class/xor_donut.py:49
↓ 2 callersFunctionderivative_b2
(T, Y)
ann_class/xor_donut.py:38
↓ 2 callersFunctionderivative_w1
(X, Z, T, Y, W2)
ann_class/xor_donut.py:42
↓ 2 callersFunctionderivative_w2
(Z, T, Y)
ann_class/xor_donut.py:34
↓ 2 callersFunctionerror_rate
(p, t)
ann_class2/tf_with_save.py:13
↓ 2 callersFunctionextract_images
Extract the images into a 4D uint8 numpy array [index, y, x, depth].
tensorflow/input_data.py:24
↓ 2 callersFunctionextract_labels
Extract the labels into a 1D uint8 numpy array [index].
tensorflow/input_data.py:47
↓ 2 callersMethodf1_score
(self, trees)
nlp_class2/rntn_theano.py:162
↓ 2 callersFunctionfit
(X, Y)
linear_regression_class/overfitting.py:18
↓ 2 callersMethodfit
(self, X, max_iter=25, eps=1e-1)
hmm_class/hmmc_scaled_concat.py:21
↓ 2 callersMethodfit
(self, X, max_iter=25, eps=1e-1)
hmm_class/hmmc_scaled_concat_diag.py:21
↓ 2 callersMethodfit
(self, X, max_iter=30, eps=1e0)
hmm_class/hmmc_concat.py:23
↓ 2 callersMethodfit
(self, X, learning_rate=1e-2, max_iter=10)
hmm_class/hmmc_theano.py:24
↓ 2 callersMethodfit
(self, X, learning_rate=1e-2, max_iter=10)
hmm_class/hmmc_theano2.py:32
↓ 2 callersMethodfit
(self, X, max_iter=10)
hmm_class/hmmc_tf.py:143
↓ 2 callersMethodfit
(self, X, Y, learning_rate=10e-3, mu=0.99, reg=10e-12, eps=10e-10, epochs=400, batch_sz=20, print_period=1, sh
rnn_class/mlp_parity.py:31
↓ 2 callersMethodfit
(self, X, learning_rate=10e-1, mu=0.99, reg=1.0, activation=T.tanh, epochs=500, show_fig=False)
rnn_class/srn_language.py:18
↓ 2 callersMethodfit
(self, X, Y)
supervised_class2/bagging_classification.py:50
↓ 2 callersMethodfit
(self, X, Y)
supervised_class2/bagging_regression.py:36
↓ 2 callersMethodfit
(self, X)
unsupervised_class3/dcgan_theano.py:471
↓ 2 callersMethodfit
(self, X, Y, sigma=1.5, lam=1, show_w=set(), Q=None)
bayesian_ml/2/probit.py:12
↓ 2 callersMethodfit
(self, X, pretrain_epochs=1)
unsupervised_class2/unsupervised.py:26
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