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some more clarifications

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pswietojanski 2015-09-28 18:19:07 +01:00
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14
00_Introduction.ipynb
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@ -49,7 +49,7 @@
"\n",
" * `mkdir -p ~/mlpractical/repos-3rd`\n",
"\n",
"And now, let us set an MLP_WDIR environmental variable (MLP Working DIRectory) that denotes an absolute path of working dir pointing to `~/mlpractial`, **add the below line** to your `~/.bashrc` file (if it does not exists, create one!):\n",
"And now, let us set an MLP_WDIR environmental variable (MLP Working DIRectory) that will keep an absolute path of working dir pointing to `~/mlpractial`, **add the below line** to your `~/.bashrc` file (if it does not exists, create one!):\n",
"\n",
"```\n",
"export MLP_WDIR=~/mlpractical\n",
@ -66,11 +66,13 @@
" * `git clone https://github.com/pypa/virtualenv`\n",
" * Enter the cloned repository and type ```./virtualenv.py --python /usr/bin/python2.7 --no-site-packages $MLP_WDIR/venv```\n",
" * Activate the environment by typing `source ~/mlpractical/venv/bin/activate` (to leave the virtual environment one may type `decativate`)\n",
" * Environments need to be activated every time ones start the new session so add this command to your `~/.bashrc` script, by typing the below command (note, MLP_WDIR export needs to preceed this command and `.` is actually important):\n",
" * Environments need to be activated every time ones start the new session so we will now create a handy alias to it in `~/.bashrc` script, by typing the below command (note, MLP_WDIR export needs to preceed this command):\n",
" \n",
" ```. $MLP_WDIR/venv/bin/activate``` \n",
" ```alias activate_mlp=\"source $MLP_WDIR/venv/bin/activate\"```\n",
" \n",
"Then every time you open new session and want to activate the right virtual environment, simply type `activate_mlp` instead `source ~/mlpractical/venv/bin/activate`. Note, you need to re-soure the .bashrc in order alias to be visible in the current session.\n",
"\n",
"### Install packages\n",
"### Installing remaining packages\n",
"\n",
"Then, before you follow next, install/upgrade the following packages:\n",
"\n",
@ -348,7 +350,9 @@
"source": [
"### Exercise 3\n",
"\n",
"Write your own data provider `MetOfficeDataProvider` that wraps the weather data for south Scotland (could be obtained from: http://www.metoffice.gov.uk/hadobs/hadukp/data/daily/HadSSP_daily_qc.txt). The file was also downloaded and stored in `data` directory for your convenience. The provider should return a tuple `(x,t)` of the estimates over an arbitrary time windows (i.e. last N-1 days) for `x` and the N-th day as the one which model should be able to predict, `t`. For now, skip missing data-points (denoted by -99.9) and simply use the next correct value. Make sure the provider works for arbitrary `batch_size` settings, including the case where single mini-batch is equal to all datapoints in the dataset. Test the dataset in the cell below."
"Write your own data provider `MetOfficeDataProvider` that wraps the weather data for south Scotland (could be obtained from: http://www.metoffice.gov.uk/hadobs/hadukp/data/daily/HadSSP_daily_qc.txt). The file was also downloaded and stored in `data` directory for your convenience. The provider should return a tuple `(x,t)` of the estimates over an arbitrary time windows (i.e. last N-1 days) for `x` and the N-th day as the one which model should be able to predict, `t`. For now, skip missing data-points (denoted by -99.9) and simply use the next correct value. Make sure the provider works for arbitrary `batch_size` settings, including the case where single mini-batch is equal to all datapoints in the dataset. Test the dataset in the cell below.\n",
"\n",
"Tip: To follow with this exercise, copy MNISTDataProvider in dataset.py, rename it to `MetOfficeDataProvider` and reimplement necesarry parts (including the arguments you pass to the constructor)."
]
},
{