How to use parse_features method in Behave

Best Python code snippet using behave

hw1_regression.py

Source: hw1_regression.py Github

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...12 # a. lambda for weight generation--non-negative integer:13 w_lambda = int(sys.argv[1])14 # b. sigma squared for observation--arbitrary positive number:15 y_sigma2 = int(sys.argv[2])16 def parse_features(filename):17 with open(filename) as f:18 lines = f.readlines()19 features = np.asarray(20 [[float(val) for val in line.split(",")] for line in lines]21 )22 return features23 # c. 'X_train.csv'24 X_train = parse_features(sys.argv[3])25 # d. 'y_train.csv'26 y_train = parse_features(sys.argv[4])27 # e. 'X_test.csv'28 X_test = parse_features(sys.argv[5])29 # ## Posterior Estimation30 #31 # Posterior covariance matrix:32 w_cov_posterior = np.linalg.pinv(w_lambda + np.matmul(X_train.T, X_train) /​ y_sigma2)33 # ## Ridge Regression34 #35 # Given samples and observations, solve the following ridge regression problem under the given generation & observation noises36 #37 # $$38 # w_{RR} = \arg\min_w \|y - Xw\|^2 + \lambda\|w\|^2.39 # $$40 #41 # Which is42 #...

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features.py

Source: features.py Github

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...43 def storage(self):44 return "enable_{0}".format(self.identifier)45def add_feature(group, feature):46 Feature(group, feature).add_options()47def parse_features(opt, group_name, features):48 def is_feature(dep):49 return dep['name'].find('--') >= 050 def strip_feature(dep):51 dep['name'] = dep['name'].lstrip('-')52 return dep53 features = [strip_feature(dep) for dep in features if is_feature(dep)]54 group = opt.get_option_group(group_name)55 if not group:56 group = opt.add_option_group(group_name)57 [add_feature(group, feature) for feature in features]...

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hw2_classification.py

Source: hw2_classification.py Github

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...9 if len(sys.argv) != 4:10 print "usage: python hw2_classification.py X_train.csv y_train.csv X_test.csv"11 exit(1)12 ## Parse parameters:13 def parse_features(filename):14 with open(filename) as f:15 lines = f.readlines()16 features = np.asarray(17 [[float(val) for val in line.split(",")] for line in lines]18 )19 return features20 # a. 'X_train.csv'21 X_train = parse_features(sys.argv[1])22 # b. 'y_train.csv'23 y_train = parse_features(sys.argv[2]).astype(np.int).ravel()24 # c. 'X_test.csv'25 X_test = parse_features(sys.argv[3])26 # ## Maximum-Likelihood Estimation for Priors & Generative Parameters27 # Class priors:28 prior_ml = np.bincount(y_train).astype(np.float) /​ y_train.shape[0]29 # Gaussian parameters:30 class_indices = np.unique(y_train)31 mu_ml = np.asarray(32 [np.mean(X_train[y_train == class_idx], axis = 0) for class_idx in class_indices]33 )34 cov_ml = np.asarray(35 [np.cov(X_train[y_train == class_idx].T, bias=True) for class_idx in class_indices]36 )37 # Posterior probabilities for test cases:38 pdfs = [multivariate_normal(mean=mu_ml[class_idx], cov=cov_ml[class_idx]) for class_idx in class_indices]39 posteriors = np.column_stack(...

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MyPlotLib.py

Source: MyPlotLib.py Github

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1import matplotlib.pyplot as plt2import numpy as np3import seaborn as sns4def parse_features(features, data):5 good_features = []6 for elem in features:7 if elem in data:8 try:9 t = data[elem] + 110 except TypeError:11 pass12 else:13 good_features.append(elem)14 if good_features == []:15 return False16 return good_features17 18class MyPlotLib():19 def __init__(self):20 pass21 def histogram(self, data, features):22 good_features = parse_features(features, data)23 l = len(good_features)24 f = plt.figure(1)25 data = data.fillna(data.median())26 for i, feature in enumerate(good_features):27 a = plt.subplot(1, l, i + 1)28 a.hist(data[feature], color='blue', edgecolor='black',29 bins = 30)30 a.title.set_text(feature)31 plt.subplots_adjust(wspace=1)32 plt.show()33 return True34 def density(self, data, features):35 good_features = parse_features(features, data)36 f = plt.figure(1)37 data = data.fillna(data.median())38 for elem in good_features:39 subset = data[elem]40 sns.distplot(subset, hist=False, kde=True, kde_kws={'linewidth': 3},41 label = elem)42 plt.show()43 return True44 def pair_plot(self, data, features):45 good_features = parse_features(features, data)46 data = data[features]47 data = data.fillna(data.median())48 sns.set(style="ticks", color_codes=True)49 sns.pairplot(data)50 plt.show()51 return True52 def box_plot(self, data, features):53 good_features = parse_features(features, data)54 l = len(good_features)55 data = data.fillna(data.median())56 f, plo = plt.subplots(1, l, sharey=True, squeeze=False)57 for i, feature in enumerate(good_features):58 plo[0, i].boxplot(data[feature])59 plo[0, i].title.set_text(feature)60 plt.subplots_adjust(wspace=1)61 plt.show()...

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