����JFIF�����%%��� }!1AQa"q2���#B��R��$3br� %&'()*456789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz������������������������������������������������������������������������� w!1AQaq"2�B���� #3R�br� $4�%�&'()*56789:CDEFGHIJSTUVWXYZcdefghijstuvwxyz��������������������������������������������������������������������������?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|��O�������h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@��o�E��/�?��ߵE_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ ?�z�����������goڢ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?��=[�Qg�����o����Q@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y�����[����TP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,���|-��v��(���� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�������;~��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@�������?�_�����j������ (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@��o�E��/�?��ߵE_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ ?�z�����������goڢ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?��=[�Qg�����o����Q@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y�����[����TP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,���|-��v��(���� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�������;~��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@�������?�_�����j������ (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@��o�E��/�?��ߵE_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ ?�z�����������goڢ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?��=[�Qg�����o����Q@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y�����[����TP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,��������ο�O�P��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@����(���g���Y������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���V��Y|����Y����UP��@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P����,�����,��u������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j���h�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� �@���o�E��?�?����ο�U_�P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@ _�z�����������g_ڪ�?��(�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (��?�/�=[�Qe�����g����U@��P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������k�w���~���v��������� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (�� (���տ�_�����:��T�~�@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@P@������/���?��j�?�5o�%��?��� g����U@�����&O3�����a�;�^=�wH���D��/��*� �fX�I���,������k?g_���?�5o�%��?��� g����U@�F�����������*������?�o�}��Τ~g��ʀ�#V��Y������~ο�T�j��K/� ������������z��������#;�~���A�;��� w�F�����������*���տ��_�@�o��5����EU������������u�誠��W��[�����������O��?jW���@��տ���@�o��5����EM������������v�訠�#V��Y�������������V��Zv��~����vw�~���c�Q@���,��~���kgo���?�5o�%��/��� o����Q@��o�%�>�ߤ���߳����S������?��o�%�~�ߠ�d�߳����S����g�P��j��K?� _������������[� g�D����[�;�TP7���������'Ѿ���=��;/�P��j��K?� _������������[� g�D����[�;�TP���,��~���kgo���a������۔���B{���ea�`T�+ �n%Ц �����j��K?� _������������[� g�D����[�;�TP���,��~���kgo����?���%�/�~�����#����x��c�~�q�v�t`ȫ��_'h���������'�]�;{s� Pp=N= 5���%�����ڜs�����=���J��A@�����Kp�b��}��X�����4g v+:�Բ�+60�ʩ,� @�����������I �uO�����ToUv��bgUl�cP�T?�#V��Y������������j��K?� _����������!��X��]���������TK�|4��`� ��#��P\y��aa >NgL��j��K?� _������������[� g�D����[�;�TP���,��~���kgo���o�F�����$��ہ�� ��vݞr6��S�q''*02���[� g�D����[�;�TP���,��~���kgo���?�5o�%��/��� o����Q@�F�����������*(��տ���@�o��5����EE������������v�訠��������~1�o���}G�L�������5o�%��/��� o����Q@�F�����������*(��տ���@�o��5����EE5����%�˷���r�v����y�\~���)(?0���=[� i����>��gc��N=����5o�%��/��� o����Q@�F�����������*(��W��Z�l����m#���X�wn_�j`0C6윅����5o�%��/��� o����Q@��տ��y9���gbO�G�5@�n�>���#V��Y������~ο�T��V��Y����9�gc��s�T.�?Z_��[� e�D����Y�:��UP���,������k?g_����_�=_� n�~~�rI������w�,"~ԓ�!72���)( u��#V��Y������~ο�T�j��K/� ��������������K
�����Kr_���}�De>~��Z=��pjX�n[p(�"� �a,Ub�/�×�<����;��<�����K>��o���[�:����V���,��$��ϧ�*�����5O����տ��_�@�o��5����EU5��o�%����?�ꜜm�_�;>Gbs�S�����@��տ��_�@�o��5����EU ��տ��}�~�����v?�������-��o�l��~�ȥ�v����r��B1���@��տ���A�?����ggP��c�S�`@%�*����տ��_�@�o��5����EU������������u�誠7���� O���!c�|0��ёv��4�+�X�Vx�RX3��8����K>��o���[�:���u#�x��#V��Y������~ο�T�j��K/� ������������[� e�D����Y�:��UP���,������k?g_���O��[� g�D����[�:��T��=_� k����~��k����c�;����.8����c��z��Ͽ�/��zc�o����F?Z_��[� e�D����Y�:��UP���,������k?g_���C���,�v����v�o���H������(�z���w�/�����v ��T.G��Ϡ���տ��_�@�o��5����EU������������u�誠��W��[��'����%��o���:�Cڕ�R̀���j���������?�o���[�;������g0q�?��o�%�>o�_��>�gf����~4�������������u�誠�z���7�/��o���������_��[� e�D����Y�:��UP���,������k?g_���C���,�|�����o��;�Ԟ��9�l�z��ؠ3|��O�X�~���;~�q����Z�F�����������*���տ��_�@�o��5����EU!��տ��}�~����-��G��I�T�������������u�誠�#V��Y������~ο�T�j��K/� ����������#�=_� n|���KbB�gtdM��"�ڒA#n�63�6�m�P�����,���/���gS�u����#�9��5o�%��?��� g����U@��o�%�o�_�����u��'�������?��o��� ���3��?go���|m�ڇ���-S�O��x��>���^�����7����x�]_�>�qke>���m��4��7P�Yހ��
0byt3m1n1
0byt3m1n1
Path:
/
hermes
/
bosweb
/
web
/
b2920
/
robertgrove.netfirms.com
/
rg4etk
/
cache
/
[
Home
]
File: 9bdc52be430e9a3ddb202fbc0ed22e91
a:5:{s:8:"template";s:1357:"<!DOCTYPE html> <html lang="en"> <head> <meta charset="utf-8"> <meta content="width=device-width, initial-scale=1.0, maximum-scale=1.0, user-scalable=no" name="viewport"> <title>{{ keyword }}</title> <style rel="stylesheet" type="text/css">body,div,html{margin:0;padding:0;border:0;font-size:100%;vertical-align:baseline}html{font-size:100%;overflow-y:scroll;-webkit-text-size-adjust:100%;-ms-text-size-adjust:100%}*,:after,:before{-webkit-box-sizing:border-box;-moz-box-sizing:border-box;box-sizing:border-box}body{font-family:Karla,Arial,sans-serif;font-size:100%;line-height:1.6;background-repeat:no-repeat;background-attachment:fixed;background-position:center center;-webkit-background-size:cover;-moz-background-size:cover;background-size:cover}</style> </head> <body class="lightbox nav-dropdown-has-arrow"> <div id="wrapper"> <header class="header has-sticky sticky-jump" id="header"> <div class="header-wrapper"> <div class="header-bg-container fill"> <h2>{{ keyword }}</h2> </div> </div> </header> <main class="" id="main"> {{ text }} </main> <footer class="footer-wrapper" id="footer"> {{ links }} <div class="absolute-footer dark medium-text-center text-center"> <div class="container clearfix"> <div class="footer-primary pull-left"> <div class="copyright-footer"> {{ keyword }} 2022</div> </div> </div> </div> </footer> </div> </body> </html>";s:4:"text";s:11417:"Figure 1.50: Shape of the X variable. Prepare Text Data. I take the range from 1 to 30. python calculate correlation. Its convention to load the features and the targets into separate variables, X and y respectively. 6 Dataset Split [3]: By calling the method features_importance() you obtain a Python dictionary with the name of every feature and its relative importance to It accepts one mandatory parameter. How to Run a Classification Task with Naive Bayes. The below will show the shape of our features and target variables. How to split feature and label. We take a 70:30 ratio keeping 70% of the data for training and 30% for testing. split dataset in features and target variable python sv_train, sv_test, tv_train, tv_test = train_test_split (sourcevars, targetvar, test_size=0.2, random_state=0) The test_size parameter Initially, I followed this We first split the dataset into train and test. Thankfully, the train_test_split module automatically shuffles data first by default (you can override this by setting the shuffle parameter to False). A split dataset is contained in a folder containing multiple, numbered h5 files (one file per chunk) and a metadata json file with information on the shape of the full dataset and of its chunks. The h5 files are saved using the flammkuchen library (ex deepdish ). In this tutorial, youll learn how to split your Python dataset using Scikit-Learns train_test_split function. First, three examplary classifiers are initialized ( LogisticRegression, GaussianNB , and RandomForestClassifier) and used to initialize a soft-voting VotingClassifier with weight Box plots. In this tutorial, youll learn how to split your Python dataset using Scikit-Learns train_test_split function. df.shape (1728, 7) # There are 1728 rows and 7 columns in the dataset. Loser rank. To begin, you will fit a linear regression with just one feature: 'fertility', which is the average number of children a woman in a given country gives birth to. This package has been developed in the Portugues lab for volumetric calcium imaging data. To generate a clustering dataset, the method will require the following parameters: n_samples: the number of samples/rows. All you have to do next is to separate your X_train, y_train etc. There are no missing values in any of the variables. It involves the following steps: Create the transform object, e.g. As in Chapter 1, the dataset has been preprocessed. Conclusion. Root Node - It represents the entire population or sample and this further gets divided into two or more homogeneous sets. A minimal package for saving and reading large HDF5-based chunked arrays. correlation plot python seaborn. I came across a credit card fraud dataset on Kaggle and built a classification model to predict fraudulent transactions. Its important to identify the important features from a dataset and eliminate the less important features that dont improve model accuracy. Generally in machine learning, the features of a dataset are represented by the variable X. The Python split () function can extract multiple pieces of information from an individual string and assign each to a separate variable. There is specific distinction you need to make, which is Target Variable needs to be ordinal and rest of the variables can be differently imputed. Scikit-learn is a free machine learning library for Python. To do so, both the feature and target vectors (X #split dataset in features and target variable feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree'] X = pima [feature_cols] # a MinMaxScaler. In the previous points we see how all the variables in the dataset, except the target variable, are continuous numerical. correlation matrix in python. From the basic statistical values we can see that none of the variables follows a normal distribution, since none has mean 0 and standard deviation 1. Manually managing the scaling of the target variable involves creating and applying the scaling object to the data manually. Next, youll learn how to split the dataset into train and test datasets. There are numerous ways to calculate feature importance in Python. Here we initialize the Linear Regression model. What is the best course of action to render this dataset usable for machine learning? If int, represents the absolute number of test samples. The following example presents a paragraph and turns each sentence into a variable: Example. The dataset contains 10,000 instances and 11 features. Add the target variable column to the dataframe. Modified 2 years, 10 months ago. Similarly, the labels of a dataset are referred to by the variable y. Best pract So, at first, we would be discussing the training data. This method is used Feature selection is often straightforward when working with real-valued data, such as using the Pearsons correlation coefficient, but can be challenging when working with categorical data. Notice that in our case all columns except healthy are features that we want to use for the 3. Lets consider the code below to understand: Firstly, download the dataset here: Linear_x_train.csv The following example uses the chi squared (chi^2) statistical test for non-negative features to select four of the best features from the Pima Indians onset of diabetes dataset:#Feature Extraction with Univariate Statistical Tests (Chi-squared for classification) #Import the required packages #Import pandas to read csv import pandas #Import numpy for C++ and Python Professional Handbooks : A platform for C++ and Python Engineers, where they can contribute their C++ and Python experience along with tips and Decision Tree Implementation in Python. Feature matrix: It is the collection of features, in case there are more than one. Drop the missing values from lng_df with .dropna () from pandas. Manual Transform of the Target Variable. split dataset in features and target variable python x.shape. Example: These datasets have a certain resemblance with the packages present as part of Python 3.6 and more. This package has been developed in the Portugues lab for volumetric calcium imaging data. Once the X variable had been defined, I normalised it to ensure that all of the values in it are from zero to one:-. pandas get correlation between all columns. In the previous points we see how all the variables in the dataset, except the target variable, are continuous numerical. Manually, you can use pd.DataFrame constructor, giving a numpy array (data) and a list of the names of the columns (columns).To have everything in one DataFrame, you can concatenate the features and the target into one numpy array with np.c_[] (note the []):. Now, split the dataset into features and target variable as follows . correlation coefficient python numpy example. As for any data analytics problem, we start by cleaning the dataset and eliminating all the null and missing values from the data. We use training data to basically train our model. How to split the dataset based on features? x.head () Input X y.head () Output Y Now that we have our input and output vectors ready, we can split from sklearn.feature_extraction.text import TfidfVectorizer vectorizer = TfidfVectorizer() matrix = vectorizer.fit_transform(df.ingredient_list) X = matrix y = df['is_indian'] Now, I split the dataset into training and test sets. A minimal package for saving and reading large HDF5-based chunked arrays. Ask Question Asked 2 years, 10 months ago. X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.3,random_state=123) Initializing Linear Regression Model. The Python split () function can extract multiple pieces of information from an individual string and assign each to a separate variable. The dataset contains 30 columns, Class is the target variable, while all others are features of the dataset. It demonstrates that the value of y is dependent on the value of a, b, and c. So, y is referred to as dependent feature or variable and a, b, and c are independent features or Train/Test split is the next step. #split dataset in features and target variable feature_cols = ['pregnant', 'insulin', 'bmi', 'age','glucose','bp','pedigree'] X = pima[feature_cols] # Features y = correlation matrix python. The following snippet concatenates predictors and the target variable into a single data frame: df = pd.concat([ pd.DataFrame(data.data, columns=data.feature_names), pd.DataFrame(data.target, columns=['y']) ], axis=1) df.head() Calling head() results in the following output: Image 1 Head of Breast cancer dataset (image by author) 1. import numpy as np import pandas as pd from sklearn.datasets import load_iris # save load_iris() Recursive Binary Partitions. If you are new to cleaning text data, see this post: 2. It returns a list of NumPy arrays, other sequences, or SciPy sparse matrices if appropriate: arrays is the sequence of lists, NumPy arrays, pandas DataFrames, or similar array-like objects that hold the data you want to split. All these objects together make up the dataset and must be of the same length. 1 ##### # # Gds2 stream format is composed of variable length records. Using Scikit-Learn in Python. February 22, 2022. Instructions. correlation for specific columns. In scikit-learn, this consists of separating your full dataset into Features and Target. correlation with specific columns. We should start with separating features for our model from the target variable. We will use Extra Tree Classifier in The problem is that the columns holding the player names in my data are labeled 'Winner' and 'Loser'. Splitting Dataset. train_test_split randomly n_features: the number of features/columns. X_train, X_test, y_train, y_test = train_test_split (. And Passed as an array, each element shows the number of samples per cluster. Training data is a complete set of feature variables or the We find these three the easiest to understand. You can start by making a list of numbers using range () like this: X = list (range (15)) print (X) Then, we add more code to make another list of square values of numbers in X: y = [x * x for x in X] print (y) Now, let's apply the train_test_split function. Some models will learn calibrated probabilities as part of the training process (e.g. Introduction to Dataset in Python. If None, the value is set to the complement of the train size. The code to declare the matrix of features will be as follows: X= dataset.iloc[:,:-1].values paragraph = 'The quick brown fox jumps over the lazy dog. 5. Remember to use the code Menu omnigender definition; silver claddagh ring argos As input features, I use the matrix of TFIDF values given by the list of ingredients. This tutorial goes over the train test split procedure and how to apply it in Python. You'll learn to split data and refactor components as you create flexible wrapping components. Since the target variable here is quantitative, this is a regression problem. As in Chapter 1, the dataset has been preprocessed. If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. That's obviously a problem when trying to learn features to predict class labels. Python datasets consist of dataset object which in turn comprises metadata as part of the dataset. So, out of the data of 10000 houses, I split the data set in such a way that 8000 rows are used for training and 2000 are used for testing. ";s:7:"keyword";s:52:"split dataset in features and target variable python";s:5:"links";s:1186:"<ul><li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10347650fc6d66fc90620133f9">Photos Of Janine Donahue</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10361330fc6d60f3b14a6a">How Much Is Beer At Allegiant Stadium</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10374240fc6d6df56e12ce97abaab2c5">Kansas Lead Singer Dust In The Wind</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10341590fc6d607e576e836effb341">Innocent Amusements: The Stage Of Sufferance Summary</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10368880fc6d6ccfa177e3a3">Escena Homes For Sale Palm Springs</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10373230fc6d60">Asurion Home Protect Verizon</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10351700fc6d64a2888c21432d7d0c">Doug Jackson Sv Seeker Wife</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10346370fc6d6c1d294d3b4">Pursell Farms Wedding</a></li> <li><a href="https://www.mobiletruckmechanicnearme.org/rg4etk/10357900fc6d6a53f701227e4e">Best Beach Resorts In Cuba</a></li> </ul>";s:7:"expired";i:-1;}
© 2017 -
ZeroByte.ID
.