Python APM.fit_predict示例

示例#1

文件： test_apm.py 项目： dr-nate/msmbuilder

def test_dtype():
    X = rs.randn(100, 2)
    X32 = X.astype(np.float32)
    X64 = X.astype(np.float64)
    m1 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X32])
    m2 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X64])

    eq(m1.labels_[0], m2.labels_[0])
    eq(m1.MacroAssignments_[0], m2.MacroAssignments_[0])
    eq(m1.fit_predict([X32])[0], m2.fit_predict([X64])[0])
    eq(m1.fit_predict([X32])[0], m1.MacroAssignments_[0])

示例#2

文件： test_apm.py 项目： jadeshi/msmbuilder-1

def test_dtype():
    X = rs.randn(100, 2)
    X32 = X.astype(np.float32)
    X64 = X.astype(np.float64)
    m1 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X32])
    m2 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X64])

    eq(m1.labels_[0], m2.labels_[0])
    eq(m1.MacroAssignments_[0], m2.MacroAssignments_[0])
    eq(m1.fit_predict([X32])[0], m2.fit_predict([X64])[0])
    eq(m1.fit_predict([X32])[0], m1.MacroAssignments_[0])

示例#3

文件： test_apm.py 项目： jadeshi/msmbuilder-1

def test_rmsd():
    # test for predict using euclidean distance
    m1 = APM(n_macrostates=4, metric='rmsd', lag_time=1)
    m2 = APM(n_macrostates=4, metric='rmsd', lag_time=1)
    labels1 = m1.fit_predict([trj])
    labels2 = m2.fit([trj]).MacroAssignments_

    eq(labels1[0], labels2[0])

示例#4

文件： test_apm.py 项目： jadeshi/msmbuilder-1

def test_euclidean_10000():
    # test for predict using euclidean distance
    m1 = APM(n_macrostates=2, metric='euclidean', lag_time=10)
    m2 = APM(n_macrostates=2, metric='euclidean', lag_time=10)
    data = rs.randn(10000, 2)
    labels1 = m1.fit_predict([data])
    labels2 = m2.fit([data]).MacroAssignments_
    eq(labels1[0], labels2[0])

示例#5

文件： test_apm.py 项目： evanfeinberg/msmbuilder

def test_rmsd():
    # test for predict using euclidean distance
    m1 = APM(n_macrostates=4, metric='rmsd', lag_time=1)
    m2 = APM(n_macrostates=4, metric='rmsd', lag_time=1)
    labels1 = m1.fit_predict([trj])
    labels2 = m2.fit([trj]).MacroAssignments_

    eq(labels1[0], labels2[0])

示例#6

文件： test_apm.py 项目： evanfeinberg/msmbuilder

def test_euclidean_10000():
    # test for predict using euclidean distance
    m1 = APM(n_macrostates=2, metric='euclidean', lag_time=10)
    m2 = APM(n_macrostates=2, metric='euclidean', lag_time=10)
    data = np.random.randn(10000, 2)
    labels1 = m1.fit_predict([data])
    labels2 = m2.fit([data]).MacroAssignments_
    eq(labels1[0], labels2[0])

示例#7

文件： test_apm.py 项目： Eigenstate/msmbuilder

def test_rmsd():
    # test for predict using rmsd
    m1 = APM(n_macrostates=4, metric='rmsd', lag_time=1, random_state=rs)
    m2 = APM(n_macrostates=4, metric='rmsd', lag_time=1, random_state=rs)
    labels1 = m1.fit_predict([trj])
    labels2 = m2.fit([trj]).MacroAssignments_

    eq(labels1[0], labels2[0])

示例#8

def test_rmsd():
    # test for predict using rmsd
    m1 = APM(n_macrostates=4, metric='rmsd', lag_time=1, random_state=rs)
    m2 = APM(n_macrostates=4, metric='rmsd', lag_time=1, random_state=rs)
    labels1 = m1.fit_predict([trj])
    labels2 = m2.fit([trj]).MacroAssignments_

    eq(labels1[0], labels2[0])

示例#9

文件： test_apm.py 项目： evanfeinberg/msmbuilder

def test_dtype():
    X = np.random.RandomState(1).randn(100, 2)
    X32 = X.astype(np.float32)
    X64 = X.astype(np.float64)
    m1 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X32])
    m2 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X64])

    eq(m1.labels_[0], m2.labels_[0])
    eq(m1.MacroAssignments_[0], m2.MacroAssignments_[0])
    eq(m1.fit_predict([X32])[0], m2.fit_predict([X64])[0])
    eq(m1.fit_predict([X32])[0], m1.MacroAssignments_[0])

#Testing
#if __name__ == "__main__":
#    test_shapes()
#    test_euclidean()
#    test_euclidean_10000()
#    test_rmsd()

示例#10

文件： test_apm.py 项目： Eigenstate/msmbuilder

def test_euclidean():
    # test for predict using euclidean distance
    data = rs.randn(100, 2)
    m1 = APM(n_macrostates=2, metric='euclidean', lag_time=1, random_state=rs)
    m2 = APM(n_macrostates=2, metric='euclidean', lag_time=1, random_state=rs)

    labels1 = m1.fit_predict([data])
    labels2 = m2.fit([data]).MacroAssignments_
    eq(labels1[0], labels2[0])

示例#11

文件： test_apm.py 项目： ryjin/msmbuilder

def test_dtype():
    X = np.random.RandomState(1).randn(100, 2)
    X32 = X.astype(np.float32)
    X64 = X.astype(np.float64)
    m1 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X32])
    m2 = APM(n_macrostates=3, metric='euclidean', lag_time=1).fit([X64])

    eq(m1.labels_[0], m2.labels_[0])
    eq(m1.MacroAssignments_[0], m2.MacroAssignments_[0])
    eq(m1.fit_predict([X32])[0], m2.fit_predict([X64])[0])
    eq(m1.fit_predict([X32])[0], m1.MacroAssignments_[0])


#Testing
#if __name__ == "__main__":
#    test_shapes()
#    test_euclidean()
#    test_euclidean_10000()
#    test_rmsd()