code stringlengths 3 6.57k |
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np.array([1, 2, 3, 4, 5]) |
np.array([1, 3, 2, 4, 5]) |
corrs.pearson_coeff(R, Q) |
pearsonr(R, Q) |
self.assertEqual(test_result, real_result) |
main() |
Test_Spearman() |
test_spearman_coeff.test_spearman() |
Test_Weighted_Spearman() |
test_weighted_spearman_coeff.test_weighted_spearman() |
Test_Pearson() |
test_pearson_coeff.test_pearson() |
Test_WS() |
test_ws.test_ws() |
main() |
str (required) |
int (required) |
int (required) |
__init__(self,assembly_type,assembly_kccs,assembly_kpis) |
PartType(AssemblySystem) |
int (required) |
int (required) |
int (required) |
__init__(self,assembly_type,assembly_kccs,assembly_kpis,part_name,part_type,voxel_dim,voxel_channels,point_dim) |
super() |
__init__(assembly_type,assembly_kccs,assembly_kpis) |
get_nominal_cop(self,file_name) |
str (required) |
pd.read_csv(file_name, sep=',',header=None) |
get_nominal_cop_database(self,conn_str,table_name) |
str (required) |
str (required) |
create_engine(conn_str) |
pd.read_sql_query(squery,con=engine) |
VRMSimulationModel(PartType) |
float (required) |
str (optional) |
int (optional) |
__init__(self,assembly_type,assembly_kccs,assembly_kpis,part_name,part_type,voxel_dim,voxel_channels,point_dim,noise_level,noise_type='uniform',convergency_flag=1) |
super() |
__init__(assembly_type,assembly_kccs,assembly_kpis,part_name,part_type,voxel_dim,voxel_channels,point_dim) |
kpi_calculator(self,cop_data,kpi_params=[]) |
f(Cop) |
list (optional) |
SecondTableInA(object) |
Init(self, buf, pos) |
flatbuffers.table.Table(buf, pos) |
ReferToC(self) |
flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4) |
self._tab.Indirect(o + self._tab.Pos) |
TableInC() |
obj.Init(self._tab.Bytes, x) |
SecondTableInAStart(builder) |
builder.StartObject(1) |
SecondTableInAAddReferToC(builder, referToC) |
builder.PrependUOffsetTRelativeSlot(0, flatbuffers.number_types.UOffsetTFlags.py_type(referToC) |
SecondTableInAEnd(builder) |
builder.EndObject() |
find_packages(exclude=('tests', 'tests.*') |
matplotlib.style.use('ggplot') |
plt.style.use('ggplot') |
pd.read_csv('/home/dipanjan/DAT210x/Module3/Datasets/wheat.data', index_col=0) |
wheat_df.plot.scatter(x='area', y='perimeter') |
wheat_df.plot.scatter(x='groove', y='asymmetry') |
wheat_df.plot.scatter(x='compactness', y='width') |
wheat_df.plot.scatter(x='compactness', y='width', marker='o') |
plt.show() |
sys.path.append(os.path.dirname(__file__) |
sys.path.pop() |
homo_lumo_mix(C, nocc, beta) |
Exception("Mixing beta must be in [0, 1]") |
C.copy() |
get_dm(C, nel) |
get_JK(is_fitted, g, D) |
if(is_fitted) |
np.einsum("Pls,ls->P", g, D) |
np.einsum("mnP,P->mn", np.swapaxes(g, 0, 2) |
np.einsum("Pns,ls->Pnl", g, D) |
np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2) |
return (J, K) |
np.einsum("pqrs,rs->pq", g, D) |
np.einsum("prqs,rs->pq", g, D) |
jk.getJK_np_Dshift(g, D - np.diag(np.diag(D) |
return (J, K) |
get_JK_uhf(is_fitted, g, Ds) |
if (is_fitted == True) |
np.einsum("Pls,ls->P", g, Dtot) |
np.einsum("mnP,P->mn", np.swapaxes(g, 0, 2) |
np.einsum("Pns,ls->Pnl", g, Da) |
np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2) |
np.einsum("Pns,ls->Pnl", g, Db) |
np.einsum('mlP,Pnl->mn', np.swapaxes(g, 0, 2) |
np.einsum("pqrs, rs -> pq", g, Dtot) |
np.einsum("prqs, rs -> pq", g, Da) |
np.einsum("prqs, rs -> pq", g, Db) |
get_fock(H, g, D) |
get_JK(len(g.shape) |
diis_update(F_prev_list, r_prev_list) |
diis_solver(r_prev_list) |
enumerate(F_prev_list) |
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