Script to generate data header file

Applications/GesturePod/training/README.md

@@ -83,6 +83,13 @@ the GesturePod. Alternately, update
 ```EdgeML/Applications/GesturePod/onComputer/src/data.h``` to simulate inference
 of the new model on your computer.
 
+To generate a `data.h`, you could follow the below: 
+Save the W, B, Z matrices as Tab Separated Values (tsv) after training a model, in the path of `genDataHeader.py`.
+Note the value of `gamma`.
+```
+python genDataHeader.py [gamma]
+```
+This will generate the `data.h`
 
 ## Dependencies
 To communicate with the MPU6050, we use [jrowberg's](https://github.com/jrowberg/i2cdevlib) ```i2cdevlib``` library.  Last tested with commit [900b8f9](https://github.com/jrowberg/i2cdevlib/tree/900b8f959e9fa5c3126e0301f8a61d45a4ea99cc).

Applications/GesturePod/training/dataFileTemplate.py

@@ -0,0 +1,92 @@
+'''
+Copyright (c) Microsoft Corporation. All rights reserved.
+Licensed under the MIT license.
+
+dataFileTemplate.py has the template for data.h 
+DO NOT MODIFY THIS
+
+'''
+def populateDataFileTemplate(valueDict):
+    gamma = valueDict['gamma']
+    featDim = valueDict['featDim']
+    ldDim = valueDict['ldDim']
+    ldProjectionMatrix = valueDict['ldProjectionMatrix']
+    numPrototypes = valueDict['numPrototypes']
+    prototypeMatrix = valueDict['prototypeMatrix']
+    numLabels = valueDict['numLabels']
+    prototypeLabelMatrix = valueDict['prototypeLabelMatrix']
+    
+    template = '''/*
+ * This is an autogenerated file. Modifications
+ * might not be persistent.
+ */
+namespace protoNNParam {
+
+#ifndef __TEST_PROTONN__
+    /** Gamma for gaussian kernel */
+    const PROGMEM float gamma = %s;
+    /** Low Dimensional Projection Matrix */
+    const PROGMEM unsigned int featDim = %s;
+    const PROGMEM unsigned int ldDim = %s;
+
+    /**
+    * Projectino Matrix (W)
+    * d_cap x d flattened (dimension of 2D array)
+    * ldDim x featDim
+    */
+    const PROGMEM  float ldProjectionMatrix[]  = {%s};
+    ''' % (gamma, featDim, ldDim, ldProjectionMatrix)
+
+    template += '''
+    /**
+     * Prototypes (B)
+     * m x d_cap flattened (dimension of 2D array)
+     * numPrototypes x d_cap
+     */
+    const PROGMEM float prototypeMatrix[] = {%s};
+    /** Number of prototypes (m) */
+    const PROGMEM unsigned int numPrototypes = %s;
+    ''' % (prototypeMatrix, numPrototypes)
+
+    template += '''
+    /**
+     * Prototype Lables (Z)
+     * m x L (dimension of 2D array)
+     * numLabels x numPrototypes
+     */
+    const PROGMEM float prototypeLabelMatrix[] = {%s};
+    /** Number of output labels, (L). */
+    const PROGMEM unsigned int numLabels = %s; // 0,1,2,3,4,5
+    ''' % (prototypeLabelMatrix, numLabels)
+
+    template += '''
+#else
+    const PROGMEM float = 1.0;
+    const PROGMEM unsigned int featDim = 10;
+    const PROGMEM unsigned int ldDim = 5;
+    // Row Major (X.x)
+    const PROGMEM  float ldProjectionMatrix[]  = {
+        0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,
+        1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,
+        2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,
+        3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,
+        4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,
+    };
+    // Column Major
+    const PROGMEM unsigned int numPrototypes = 3;
+    const PROGMEM float prototypeMatrix[] = {
+        -1.0,-0.5,0.0,0.5,1.0,
+        -2.0,-1.0,0.0,1.0,2.0,
+        -7.51,-7.51,-7.51,-7.51,-7.51,
+    };
+    // column major
+    const PROGMEM unsigned int numLabels = 4;
+    const PROGMEM float prototypeLabelMatrix[] = {
+        0.96,0.01,0.01,0.02,
+        0.02,0.94,0.02,0.02,
+        0.10,0.15,0.55,0.20,
+    };
+#endif
+};
+    '''
+    return template

Applications/GesturePod/training/genDataHeader.py

@@ -0,0 +1,102 @@
+'''
+Copyright (c) Microsoft Corporation. All rights reserved.
+Licensed under the MIT license.
+
+genDataHeader.py generates data.h
+'''
+
+import numpy as np
+import pandas as pd
+from dataFileTemplate import populateDataFileTemplate
+import sys
+
+
+def loadTLCMatrices(dfolder):
+    '''
+    Loads Matrices B, W and Z from TLC format
+    '''
+    # W is stored as d_cap x d
+    df = pd.read_csv(dfolder + 'W', sep='\t', header=None)
+    W = np.matrix(df)
+    d_cap = W.shape[0]
+
+    # B is stored as d_cap x m
+    df = pd.read_csv(dfolder + 'B', sep='\t', header=None)
+    B = np.matrix(df)
+    m = B.shape[1]
+    assert(d_cap == B.shape[0])
+
+    # Z is stored as L x m
+    df = pd.read_csv(dfolder + 'Z', sep='\t', header=None)
+    Z = np.matrix(df)
+    assert(Z.shape[1] == m)
+
+    return W, B, Z
+
+
+def createDataFile(W, B, Z, gamma, outfile='data.h'):
+    '''
+    Exports the provided matrices loaded, into a temp file
+    so that they can be directly copied onto the MKR1000
+    board's data.h file.
+    Required input matrix dimensions:
+    W: d_cap x d
+    B: d_cap x m
+    Z: L x m
+
+    Created output dimensions:
+    W: d_cap x d
+    B: m x d_cap (i.e column major)
+    Z: m x L (i.e. column major)
+    '''
+    valueDict = {}
+    valueDict['gamma'] = gamma
+    d_cap = W.shape[0]
+    d = W.shape[1]
+    valueDict['featDim'] = '%d' % (d)
+    valueDict['ldDim'] = '%d' % (d_cap)
+    WStr = '\n\t\t'
+    for i in range(0, d_cap):
+        for j in range(0, d):
+            WStr += str(W[i, j]) + ','
+        WStr += '\n\t\t'
+    valueDict['ldProjectionMatrix'] = WStr[:-1]
+
+    assert(B.shape[0] == d_cap)
+    m = B.shape[1]
+    valueDict['numPrototypes'] = '%d' % (m)
+    BStr = '\n\t\t'
+    for i in range(0, m):
+        for j in range(0, d_cap):
+            # Column major (j, i)
+            BStr += '%f' % (B[j, i]) + ','
+        BStr += '\n\t\t'
+    valueDict['prototypeMatrix'] = BStr[:-1]
+    assert(Z.shape[1] == m)
+    L = Z.shape[0]
+    valueDict['numLabels'] = L
+    ZStr = '\n\t\t'
+    for i in range(0, m):
+        for j in range(0, L):
+            # Columns major
+            ZStr += '%f' % (Z[j, i]) + ','
+        ZStr += '\n\t\t'
+    valueDict['prototypeLabelMatrix'] = ZStr[:-2]
+    template = populateDataFileTemplate(valueDict)
+    fin = open(outfile, 'w')
+    fin.write(template)
+    fin.close()
+
+
+
+def automaticExport():
+    # Copy W, B, Z from Debug
+    gamma = sys.argv[1]
+    dfolder = './'
+    W, B, Z = loadTLCMatrices(dfolder)
+    # Create a new file with gamma in it
+    createDataFile(W, B, Z, gamma, 'data.h')
+
+
+if __name__ == '__main__':
+    automaticExport()