From e13676f6f797fa6103997a9e2c8dc07dadb78c30 Mon Sep 17 00:00:00 2001 From: popo Date: Thu, 26 Apr 2012 02:57:08 +0000 Subject: [PATCH] fix typos in python/README. --- python/README | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/python/README b/python/README index d2ce9e8..40fe87d 100644 --- a/python/README +++ b/python/README @@ -63,7 +63,7 @@ in liblinearutil.py and the usage is the same as the LIBLINEAR MATLAB interface. >>> save_model('heart_scale.model', m) >>> m = load_model('heart_scale.model') >>> p_label, p_acc, p_val = predict(y, x, m, '-b 1') ->>> ACC, MSE, SCC = evaluations(y, p_val) +>>> ACC, MSE, SCC = evaluations(y, p_label) # Getting online help >>> help(train) @@ -76,7 +76,7 @@ carefully. >>> prob = problem([1,-1], [{1:1, 3:1}, {1:-1,3:-1}]) >>> param = parameter('-c 4') >>> m = liblinear.train(prob, param) # m is a ctype pointer to a model -# Convet a Python-fromat instance to feature_nodearray, a ctypes structure +# Convert a Python-format instance to feature_nodearray, a ctypes structure >>> x0, max_idx = gen_feature_nodearray({1:1, 3:1}) >>> label = liblinear.predict(m, x0) @@ -110,7 +110,7 @@ LIBLINEAR shared library: - class feature_node: - Construct an feature_node. + Construct a feature_node. >>> node = feature_node(idx, val) @@ -136,9 +136,9 @@ LIBLINEAR shared library: - class problem: - Construct an problem instance + Construct a problem instance - >>> prob = problem(y, x, [bias=-1]) + >>> prob = problem(y, x [,bias=-1]) y: a Python list/tuple of l labels (type must be int/double). @@ -148,7 +148,7 @@ LIBLINEAR shared library: bias: if bias >= 0, instance x becomes [x; bias]; if < 0, no bias term added (default -1) - You can alos modify the bias value by + You can also modify the bias value by >>> prob.set_bias(1) @@ -157,7 +157,7 @@ LIBLINEAR shared library: - class parameter: - Construct an parameter instance + Construct a parameter instance >>> param = parameter('training_options') @@ -210,7 +210,7 @@ To use utility functions, type >>> from liblinearutil import * The above command loads - train() : train an linear model + train() : train a linear model predict() : predict testing data svm_read_problem() : read the data from a LIBSVM-format file. load_model() : load a LIBLINEAR model. @@ -233,10 +233,10 @@ The above command loads training_options: a string in the same form as that for LIBLINEAR command mode. - prob: an problem instance generated by calling + prob: a problem instance generated by calling problem(y, x). - param: an parameter instance generated by calling + param: a parameter instance generated by calling parameter('training_options') model: the returned model instance. See linear.h for details of this @@ -273,7 +273,7 @@ The above command loads predicting_options: a string of predicting options in the same format as that of LIBLINEAR. - model: an model instance. + model: a model instance. p_labels: a list of predicted labels @@ -284,7 +284,7 @@ The above command loads p_vals: a list of decision values or probability estimates (if '-b 1' is specified). If k is the number of classes, for decision values, each element includes results of predicting k binary-class - SVMs. if k = 2 and solver is not MCSVM_CS, only one decision value + SVMs. If k = 2 and solver is not MCSVM_CS, only one decision value is returned. For probabilities, each element contains k values indicating the probability that the testing instance is in each class. Note that the order of classes here is the same as 'model.label' -- 2.40.0