import?numpy?as?np
from?scipy?import?stats
from?sklearn.metrics?import?f1_score?classification_report

#?X?data?shape
#?array（[[?13.04681517??14.74115241]
#????????[?13.40852019??13.7632696?]
#????????[?14.19591481??15.85318113]
#????????[?14.91470077??16.17425987]
#????????[?13.57669961??14.04284944]]）


def?select_threshold（X?Xval?yval）:
????“““use?CV?data?to?find?the?best?epsilon
????Returns:
????????e:?best?epsilon?with?the?highest?f-score
????????f-score:?such?best?f-score
????“““
????#?create?multivariate?model?using?training?data
????mu?=?X.mean（axis=0）
????cov?=?np.cov（X.T）
????multi_normal?=?stats.multivariate_normal（mu?cov）

????#?this?is?key?use?CV?data?for?fine?tuning?hyper?parameters
????pval?=?multi_normal.pdf（Xval）

????#?set?up?epsilon?candidates
????epsilon?=?np.linspace（np.min（pval）?np.max（pval）?num=10000）

????#?calculate?f-score
????fs?=?[]
????for?e?in?epsilon:
????????y_pred?=?（pval?<=?e）.astype（‘int‘）
????????fs.append（f1_score（yval?y_pred））

????#?find?the?best?f-score
????argmax_fs?=?np.argmax（fs）

????return?epsilon[argmax_fs]?fs[argmax_fs]


def?predict（X?Xval?e?Xtest?ytest）:
????“““with?optimal?epsilon?combine?X?Xval?and?predict?Xtest
????Returns:
????????multi_normal:?multivariate?normal?model
????????y_pred:?prediction?of?test?data
????“““
????Xdata?=?np.concatenate（（X?Xval）?axis=0）

????mu?=?Xdata.mean（axis=0）
????cov?=?np.cov（Xdata.T）
????multi_normal?=?stats.multivariate_normal（mu?cov）

????#?calculate?probability?of?test?data
????pval?=?multi_normal.pdf（Xtest）
????y_pred?=?（pval?<=?e）.astype（‘int‘）

????print（classification_report（ytest?y_pred））

????return?multi_normal?y_pred

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----
?????目錄???????????0??2017-10-19?21:59??ex1-linear?regression\
?????目錄???????????0??2017-10-19?21:59??ex1-linear?regression\.ipynb_checkpoints\
?????文件??????242296??2017-09-30?19:39??ex1-linear?regression\.ipynb_checkpoints\1.linear_regreesion-checkpoint.ipynb
?????文件??????267341??2017-09-30?19:39??ex1-linear?regression\.ipynb_checkpoints\1.linear_regreesion_v1-checkpoint.ipynb
?????文件???????54033??2017-09-30?19:39??ex1-linear?regression\.ipynb_checkpoints\2-?batch?gradient?decent-checkpoint.ipynb
?????文件??????189637??2017-09-30?19:39??ex1-linear?regression\.ipynb_checkpoints\3-?optional?section-checkpoint.ipynb
?????文件???????88765??2017-09-30?19:39??ex1-linear?regression\.ipynb_checkpoints\4-?tensoflow?batch?gradient?decent-checkpoint.ipynb
?????文件??????140749??2017-10-19?00:06??ex1-linear?regression\.ipynb_checkpoints\ML-Exercise1-checkpoint.ipynb
?????文件??????265724??2017-10-18?21:01??ex1-linear?regression\1.linear_regreesion_v1.ipynb
?????文件??????140749??2017-10-19?00:06??ex1-linear?regression\ML-Exercise1.ipynb
?????文件??????489928??2017-09-27?22:01??ex1-linear?regression\ex1.pdf
?????文件????????1456??2017-09-27?22:01??ex1-linear?regression\ex1data1.txt
?????文件?????????704??2017-09-27?22:01??ex1-linear?regression\ex1data2.txt
?????目錄???????????0??2017-10-19?21:59??ex2-logistic?regression\
?????目錄???????????0??2017-10-19?21:59??ex2-logistic?regression\.ipynb_checkpoints\
?????文件???????46036??2017-09-30?19:39??ex2-logistic?regression\.ipynb_checkpoints\1-?visualize?data-checkpoint.ipynb
?????文件??????302807??2017-09-30?19:39??ex2-logistic?regression\.ipynb_checkpoints\1.?logistic_regression_v1-checkpoint.ipynb
?????文件??????108205??2017-09-30?19:39??ex2-logistic?regression\.ipynb_checkpoints\2-?logistic?regression-checkpoint.ipynb
?????文件??????295061??2017-09-30?19:39??ex2-logistic?regression\.ipynb_checkpoints\2.?logistic_regression-checkpoint.ipynb
?????文件???????78000??2017-09-30?19:39??ex2-logistic?regression\.ipynb_checkpoints\3-?regularized?logistic?regression-checkpoint.ipynb
?????文件??????180797??2017-09-30?19:39??ex2-logistic?regression\.ipynb_checkpoints\4-?experiment?with?lambda?constant?for?regularization-checkpoint.ipynb
?????文件??????101947??2017-10-18?21:54??ex2-logistic?regression\.ipynb_checkpoints\ML-Exercise2-v1-checkpoint.ipynb
?????文件??????????78??2017-10-19?00:08??ex2-logistic?regression\.ipynb_checkpoints\Untitled-checkpoint.ipynb
?????文件??????301119??2017-10-18?21:23??ex2-logistic?regression\1.?logistic_regression_v1.ipynb
?????文件??????101947??2017-10-18?21:54??ex2-logistic?regression\ML-Exercise2-v1.ipynb
?????文件????????2738??2017-10-19?00:08??ex2-logistic?regression\Untitled.ipynb
?????文件??????233661??2017-09-27?22:01??ex2-logistic?regression\ex2.pdf
?????文件????????3875??2017-09-27?22:01??ex2-logistic?regression\ex2data1.txt
?????文件????????2351??2017-09-27?22:01??ex2-logistic?regression\ex2data2.txt
?????目錄???????????0??2017-10-19?23:24??ex3-neural?network\
?????目錄???????????0??2017-10-19?21:59??ex3-neural?network\.ipynb_checkpoints\
............此處省略124個文件信息