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switched examples over to the new mat() method.

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This commit is contained in:
Davis King 2012-12-23 09:25:22 -05:00
parent a5d3021816
commit d7df21a8e1
7 changed files with 15 additions and 15 deletions
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4
examples/assignment_learning_ex.cpp
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@ -177,8 +177,8 @@ int main()
{
// Predict the assignments for the LHS and RHS in samples[i].
std::vector<long> predicted_assignments = assigner(samples[i]);
cout << "true labels: " << trans(vector_to_matrix(labels[i]));
cout << "predicted labels: " << trans(vector_to_matrix(predicted_assignments)) << endl;
cout << "true labels: " << trans(mat(labels[i]));
cout << "predicted labels: " << trans(mat(predicted_assignments)) << endl;
}
// We can also use this tool to compute the percentage of assignments predicted correctly.

4
examples/custom_trainer_ex.cpp
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@ -118,9 +118,9 @@ public:
}
// divide by number of +1 samples
positive_center /= sum(vector_to_matrix(labels) == +1);
positive_center /= sum(mat(labels) == +1);
// divide by number of -1 samples
negative_center /= sum(vector_to_matrix(labels) == -1);
negative_center /= sum(mat(labels) == -1);
custom_decision_function df;
df.positive_center = positive_center;

4
examples/krr_classification_ex.cpp
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@ -65,8 +65,8 @@ int main()
}
cout << "samples generated: " << samples.size() << endl;
cout << " number of +1 samples: " << sum(vector_to_matrix(labels) > 0) << endl;
cout << " number of -1 samples: " << sum(vector_to_matrix(labels) < 0) << endl;
cout << " number of +1 samples: " << sum(mat(labels) > 0) << endl;
cout << " number of -1 samples: " << sum(mat(labels) < 0) << endl;
// Here we normalize all the samples by subtracting their mean and dividing by their standard deviation.
// This is generally a good idea since it often heads off numerical stability problems and also

2
examples/matrix_expressions_ex.cpp
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@ -384,7 +384,7 @@ void custom_matrix_expressions_example(
As an aside, note that dlib contains functions equivalent to the ones we
defined above. They are:
- dlib::trans()
- dlib::vector_to_matrix()
- dlib::mat() (converts things into matrices)
- operator+ (e.g. you can say my_mat + 1)

4
examples/rank_features_ex.cpp
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@ -79,8 +79,8 @@ int main()
// Here we normalize all the samples by subtracting their mean and dividing by their standard deviation.
// This is generally a good idea since it often heads off numerical stability problems and also
// prevents one large feature from smothering others.
const sample_type m(mean(vector_to_matrix(samples))); // compute a mean vector
const sample_type sd(reciprocal(sqrt(variance(vector_to_matrix(samples))))); // compute a standard deviation vector
const sample_type m(mean(mat(samples))); // compute a mean vector
const sample_type sd(reciprocal(stddev(mat(samples)))); // compute a standard deviation vector
// now normalize each sample
for (unsigned long i = 0; i < samples.size(); ++i)
samples[i] = pointwise_multiply(samples[i] - m, sd);

8
examples/sequence_labeler_ex.cpp
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@ -228,8 +228,8 @@ int main()
// print out some of the randomly sampled sequences
for (int i = 0; i < 10; ++i)
{
cout << "hidden states: " << trans(vector_to_matrix(labels[i]));
cout << "observed states: " << trans(vector_to_matrix(samples[i]));
cout << "hidden states: " << trans(mat(labels[i]));
cout << "observed states: " << trans(mat(samples[i]));
cout << "******************************" << endl;
}
@ -251,8 +251,8 @@ int main()
// Test the learned labeler on one of the training samples. In this
// case it will give the correct sequence of labels.
std::vector<unsigned long> predicted_labels = labeler(samples[0]);
cout << "true hidden states: "<< trans(vector_to_matrix(labels[0]));
cout << "predicted hidden states: "<< trans(vector_to_matrix(predicted_labels));
cout << "true hidden states: "<< trans(mat(labels[0]));
cout << "predicted hidden states: "<< trans(mat(predicted_labels));

4
examples/using_custom_kernels_ex.cpp
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@ -160,8 +160,8 @@ int main()
}
}
cout << "samples generated: " << samples.size() << endl;
cout << " number of +1 samples: " << sum(vector_to_matrix(labels) > 0) << endl;
cout << " number of -1 samples: " << sum(vector_to_matrix(labels) < 0) << endl;
cout << " number of +1 samples: " << sum(mat(labels) > 0) << endl;
cout << " number of -1 samples: " << sum(mat(labels) < 0) << endl;
// A valid kernel must always give rise to kernel matrices which are symmetric