[Scipy-svn] r2144 - in trunk/Lib/sandbox/svm: . tests
scipy-svn at scipy.org
scipy-svn at scipy.org
Thu Aug 3 11:12:32 EDT 2006
Author: fullung
Date: 2006-08-03 10:12:13 -0500 (Thu, 03 Aug 2006)
New Revision: 2144
Modified:
trunk/Lib/sandbox/svm/dataset.py
trunk/Lib/sandbox/svm/tests/test_classification.py
trunk/Lib/sandbox/svm/tests/test_dataset.py
trunk/Lib/sandbox/svm/tests/test_regression.py
Log:
Move zipping inside the datasets.
Modified: trunk/Lib/sandbox/svm/dataset.py
===================================================================
--- trunk/Lib/sandbox/svm/dataset.py 2006-08-03 14:38:13 UTC (rev 2143)
+++ trunk/Lib/sandbox/svm/dataset.py 2006-08-03 15:12:13 UTC (rev 2144)
@@ -139,22 +139,20 @@
param.kernel_type = libsvm.PRECOMPUTED
class LibSvmRegressionDataSet(LibSvmDataSet):
- def __init__(self, origdata):
- data = map(lambda x: (x[0], convert_to_svm_node(x[1])), origdata)
+ def __init__(self, y, x):
+ origdata = zip(y, x)
+ data = [(x[0], convert_to_svm_node(x[1])) for x in origdata]
LibSvmDataSet.__init__(self, data)
class LibSvmClassificationDataSet(LibSvmDataSet):
- def __init__(self, origdata):
- labels = N.array(map(lambda x: x[0], origdata), dtype=N.intc)
- labels.sort()
- self.labels = labels
-
- data = map(lambda x: (x[0],convert_to_svm_node(x[1])), origdata)
+ def __init__(self, labels, x):
+ origdata = zip(labels, x)
+ data = [(x[0], convert_to_svm_node(x[1])) for x in origdata]
LibSvmDataSet.__init__(self, data)
class LibSvmOneClassDataSet(LibSvmDataSet):
- def __init__(self, origdata):
- data = map(lambda x: tuple([0,convert_to_svm_node(x)]), origdata)
+ def __init__(self, x):
+ data = [(0, convert_to_svm_node(y)) for y in x]
LibSvmDataSet.__init__(self, data)
class LibSvmTestDataSet:
Modified: trunk/Lib/sandbox/svm/tests/test_classification.py
===================================================================
--- trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-03 14:38:13 UTC (rev 2143)
+++ trunk/Lib/sandbox/svm/tests/test_classification.py 2006-08-03 15:12:13 UTC (rev 2144)
@@ -32,7 +32,7 @@
N.array([0, 1]),
N.array([1, 0]),
N.array([1, 1])]
- traindata = LibSvmClassificationDataSet(zip(labels, x))
+ traindata = LibSvmClassificationDataSet(labels, x)
testdata = LibSvmTestDataSet(x)
return traindata, testdata
@@ -100,7 +100,7 @@
def check_cross_validate(self):
labels = ([-1] * 50) + ([1] * 50)
x = N.random.randn(len(labels), 10)
- traindata = LibSvmClassificationDataSet(zip(labels, x))
+ traindata = LibSvmClassificationDataSet(labels, x)
kernel = LinearKernel()
model = LibSvmCClassificationModel(kernel)
nr_fold = 10
@@ -133,11 +133,11 @@
x1 = N.random.randn(len(labels1), 10)
labels2 = N.random.random_integers(0, 2, 10)
x2 = N.random.randn(len(labels2), x1.shape[1])
- trndata1 = LibSvmClassificationDataSet(zip(labels1, x1))
- trndata2 = LibSvmClassificationDataSet(zip(labels2, x2))
+ trndata1 = LibSvmClassificationDataSet(labels1, x1)
+ trndata2 = LibSvmClassificationDataSet(labels2, x2)
reflabels = N.concatenate([labels1, labels2])
refx = N.vstack([x1, x2])
- trndata = LibSvmClassificationDataSet(zip(reflabels, refx))
+ trndata = LibSvmClassificationDataSet(reflabels, refx)
testdata = LibSvmTestDataSet(refx)
return trndata, testdata, trndata1, trndata2
Modified: trunk/Lib/sandbox/svm/tests/test_dataset.py
===================================================================
--- trunk/Lib/sandbox/svm/tests/test_dataset.py 2006-08-03 14:38:13 UTC (rev 2143)
+++ trunk/Lib/sandbox/svm/tests/test_dataset.py 2006-08-03 15:12:13 UTC (rev 2144)
@@ -93,7 +93,7 @@
]
y = N.random.randn(10)
x = N.random.randn(len(y), 10)
- origdata = LibSvmRegressionDataSet(zip(y, x))
+ origdata = LibSvmRegressionDataSet(y, x)
for kernel in kernels:
# calculate expected Gram matrix
@@ -112,12 +112,12 @@
y1 = N.random.randn(10)
x1 = N.random.randn(len(y1), 10)
- origdata = LibSvmRegressionDataSet(zip(y1, x1))
+ origdata = LibSvmRegressionDataSet(y1, x1)
pcdata = origdata.precompute(kernel)
y2 = N.random.randn(5)
x2 = N.random.randn(len(y2), x1.shape[1])
- moredata = LibSvmRegressionDataSet(zip(y2, x2))
+ moredata = LibSvmRegressionDataSet(y2, x2)
morepcdata = pcdata.combine(moredata)
expt_grammat = N.empty((len(y1) + len(y2),)*2)
Modified: trunk/Lib/sandbox/svm/tests/test_regression.py
===================================================================
--- trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-03 14:38:13 UTC (rev 2143)
+++ trunk/Lib/sandbox/svm/tests/test_regression.py 2006-08-03 15:12:13 UTC (rev 2144)
@@ -32,7 +32,7 @@
N.array([0, 1]),
N.array([1, 0]),
N.array([1, 1])]
- traindata = LibSvmRegressionDataSet(zip(y, x))
+ traindata = LibSvmRegressionDataSet(y, x)
testdata = LibSvmTestDataSet(x)
model = ModelType(LinearKernel(), probability=True)
results = model.fit(traindata)
@@ -45,7 +45,7 @@
N.array([0, 1]),
N.array([1, 0]),
N.array([1, 1])]
- traindata = LibSvmRegressionDataSet(zip(labels, x))
+ traindata = LibSvmRegressionDataSet(labels, x)
testdata = LibSvmTestDataSet(x)
return traindata, testdata
@@ -85,7 +85,7 @@
def check_cross_validate(self):
y = N.random.randn(100)
x = N.random.randn(len(y), 10)
- traindata = LibSvmRegressionDataSet(zip(y, x))
+ traindata = LibSvmRegressionDataSet(y, x)
kernel = LinearKernel()
model = LibSvmEpsilonRegressionModel(kernel)
nr_fold = 10
@@ -108,11 +108,11 @@
x1 = N.random.randn(len(y1), 10)
y2 = N.random.randn(5)
x2 = N.random.randn(len(y2), x1.shape[1])
- trndata1 = LibSvmRegressionDataSet(zip(y1, x1))
- trndata2 = LibSvmRegressionDataSet(zip(y2, x2))
+ trndata1 = LibSvmRegressionDataSet(y1, x1)
+ trndata2 = LibSvmRegressionDataSet(y2, x2)
refy = N.concatenate([y1, y2])
refx = N.vstack([x1, x2])
- trndata = LibSvmRegressionDataSet(zip(refy, refx))
+ trndata = LibSvmRegressionDataSet(refy, refx)
testdata = LibSvmTestDataSet(refx)
return trndata, trndata1, trndata2, testdata
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